Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2 - Statistics
Posted by Superadmin on November 13 2019 00:15:08

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


01.1. Population and Sample


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


01.1.1 Glossary.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


01.1.2 Course notes_descriptive_statistics.pdf


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


01.2. Population and Sample.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.1. Types of Data


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.1.1 Course notes_descriptive_statistics.pdf


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.2. Types of Data.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.3. Levels of Measurement


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.4. Levels of Measurement.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.5. Categorical Variables - Visualization Techniques


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.5.1 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx




The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.6. Categorical Variables Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.6.1 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.6.2 2.3. Categorical variables. Visualization techniques_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.7. Numerical Variables - Frequency Distribution Table


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.7.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.8. Numerical Variables Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.8.1 2.4. Numerical variables. Frequency distribution table_exercise_solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.8.2 2.4. Numerical variables. Frequency distribution table_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.9. The Histogram


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.9.1 2.5. The Histogram_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.10. Histogram Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.10.1 2.5.The-Histogram-exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.10.2 2.5.The-Histogram-exercise-solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.11. Cross Table and Scatter Plot


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.11.1 2.6. Cross table and scatter plot.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.12. Cross Tables and Scatter Plots Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.12.1 2.6. Cross table and scatter plot_exercise_solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.12.2 2.6. Cross table and scatter plot_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.13. Mean, median and mode


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.13.1 2.7. Mean, median and mode_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.14. Mean, Median and Mode Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.14.1 2.7. Mean, median and mode_exercise_solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.14.2 2.7. Mean, median and mode_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.15. Skewness


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.15.1 2.8. Skewness_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.16. Skewness Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.16.1 2.8. Skewness_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.16.2 2.8. Skewness_exercise_solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.17. Variance


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.17.1 2.9. Variance_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.18. Variance Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.18.1 2.9. Variance_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.18.2 2.9. Variance_exercise_solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.19. Standard Deviation and Coefficient of Variation


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.19.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.20. Standard Deviation and Coefficient of Variation Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.20.1 2.10. Standard deviation and coefficient of variation_exercise_solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.20.2 2.10. Standard deviation and coefficient of variation_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.21. Covariance


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.21.1 2.11. Covariance_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.22. Covariance Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.22.1 2.11. Covariance_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.22.2 2.11. Covariance_exercise_solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.23. Correlation Coefficient


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.24. Correlation Coefficient Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.24.1 2.12. Correlation_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


02.24.2 2.12. Correlation_exercise_solution.xlsx



The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


03.1. Practical Example Descriptive Statistics


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


03.1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


03.2. Practical Example Descriptive Statistics Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


03.2.1 2.13. Practical-example.Descriptive-statistics-exercise-solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


03.2.2 2.13.Practical-example.Descriptive-statistics-exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.1. Introduction


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.1.1 Course notes_inferential statistics.pdf


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.2. What is a Distribution


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.2.1 Course notes_inferential statistics.pdf


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.2.2 3.2. What is a distribution_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.3. What is a Distribution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.4. The Normal Distribution


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.5. The Normal Distribution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.6. The Standard Normal Distribution


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.6.1 3.4. Standard normal distribution_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.7. The Standard Normal Distribution Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.7.1 3.4. Standard normal distribution_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.7.2 3.4. Standard normal distribution_exercise_solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.8. Central Limit Theorem


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.9. Central Limit Theorem.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.10. Standard error


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.11. Estimators and Estimates


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


04.12. Estimators and Estimates.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.1. What are Confidence Intervals


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.2. What are Confidence Intervals.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.3. Confidence Intervals; Population Variance Known; z-score


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.3.1 3.9. Population variance known, z-score_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.3.2 3.9. The z-table.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.4. Confidence Intervals; Population Variance Known; z-score; Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.4.1 3.9. Population variance known, z-score_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.4.2 3.9. The z-table.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.4.3 3.9. Population variance known, z-score_exercise_solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.5. Student's T Distribution


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.6. Student's T Distribution.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.7. Confidence Intervals; Population Variance Unknown; t-score


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.7.1 3.11. Population variance unknown, t-score_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.7.2 3.11. The t-table.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.8. Confidence Intervals; Population Variance Unknown; t-score; Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.8.1 3.11. Population variance unknown, t-score_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.8.2 3.11. Population variance unknown, t-score_exercise_solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.9. Margin of Error


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.10. Margin of Error.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.11. Confidence intervals. Two means. Dependent samples


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.11.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.12. Confidence intervals. Two means. Dependent samples Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.12.1 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.12.2 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.13. Confidence intervals. Two means. Independent samples (Part 1)


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.13.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.14. Confidence intervals. Two means. Independent samples (Part 1) Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.14.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise_solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.14.2 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.15. Confidence intervals. Two means. Independent samples (Part 2)


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.15.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.16. Confidence intervals. Two means. Independent samples (Part 2) Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.16.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise_solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.16.2 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


05.17. Confidence intervals. Two means. Independent samples (Part 3)


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


06.1. Practical Example Inferential Statistics


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


06.1.1 3.17. Practical example. Confidence intervals_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


06.2. Practical Example Inferential Statistics Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


06.2.1 3.17. Practical example. Confidence intervals_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


06.2.2 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.1. The Null vs Alternative Hypothesis


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.1.1 Course notes_hypothesis_testing.pdf


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.2. Further Reading on Null and Alternative Hypothesis.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.3. The Null vs Alternative Hypothesis.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.4. Rejection Region and Significance Level


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.4.1 Course notes_hypothesis_testing.pdf


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.5. Rejection Region and Significance Level.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.6. Type I Error and Type II Error


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.7. Type I Error and Type II Error.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.8. Test for the Mean. Population Variance Known


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.8.1 4.4. Test for the mean. Population variance known_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.9. Test for the Mean. Population Variance Known Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.9.1 4.4. Test for the mean. Population variance known_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.9.2 4.4. Test for the mean. Population variance known_exercise_solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.10. p-value


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.10.1 Online p-value calculator.pdf


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.11. p-value.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.12. Test for the Mean. Population Variance Unknown


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.13. Test for the Mean. Population Variance Unknown Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.13.1 4.6.Test-for-the-mea n.Population-variance-unknown-exercise-solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.13.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.14. Test for the Mean. Dependent Samples


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.14.1 4.7. Test for the mean. Dependent samples_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.15. Test for the Mean. Dependent Samples Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.15.1 4.7. Test for the mean. Dependent samples_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.15.2 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.16. Test for the mean. Independent samples (Part 1)


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.16.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.17. Test for the mean. Independent samples (Part 2)


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.17.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.18. Test for the mean. Independent samples (Part 2) Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.18.1 4.9. Test for the mean. Independent samples (Part 2)_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


07.18.2 4.9. Test for the mean. Independent samples (Part 2)_exercise_solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


08.1. Practical Example Hypothesis Testing


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


08.1.1 4.10.Hypothesis-testing-section-practical-example.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


08.2. Practical Example Hypothesis Testing Exercise.html


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


08.2.1 4.10. Hypothesis testing section_practical example_exercise.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing

Udemy The Data Science Course 2018: Complete Data Science Bootcamp Part2

with Iliya


08.2.2 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx


The Data Science Course 2019: Complete Data Science Bootcamp
Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

What you'll learn::
  • The course provides the entire toolbox you need to become a data scientist
  • Impress interviewers by showing an understanding of the data science field
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Perform linear and logistic regressions in Python
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Learn how to pre-process data
  • Start coding in Python and learn how to use it for statistical analysis
  • Carry out cluster and factor analysis
  • Apply your skills to real-life business cases
  • Unfold the power of deep neural networks
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
      
Course Contents
01. Part 2 Statistics 02. Statistics - Descriptive Statistics 03. Statistics - Practical Example Descriptive Statistics 04. Statistics - Inferential Statistics Fundamentals 05. Statistics - Inferential Statistics Confidence Intervals 06. Statistics - Practical Example Inferential Statistics 07. Statistics - Hypothesis Testing 08. Statistics - Practical Example Hypothesis Testing