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DEMO - Applied Data Science with R
DEMO - Applied Data Science with RAnalytics Courses |
| Categories | Most Recent | Top Rated | Popular Courses |
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| Superadmin | 01.01.70 | 556 | No Rating | |
| Description | ||||
| Learn how to execute an end-to-end data science project and deliver business results "Data Science is the sexiest job of the 21st century - It has exciting work and incredible pay". Learning Data Science though is not an easy task. The field traverses through Computer Science, Programming, Information Theory, Statistics and Artificial Intelligence. College/University courses in this field are expensive. Becoming a Data Scientist through self-study is challenging since it requires going through multiple books, websites, searches and exercises and you will still end up feeling "not complete" at the end of it. So how do you acquire full-stack Data Science skills that will get you a and give you the confidence to execute it? Applied Data Science with R addresses the problem. This course provides extensive, end-to-end coverage of all activities performed in a Data Science project. If teaches application of the latest techniques in data acquisition, transformation and predictive analytics to solve real world business problems. The goal of this course is to teach practice rather than theory. Rather than deep dive into formulae and derivations, it focuses on using existing libraries and tools to produce solutions. It also keeps things simple and easy to understand. Through this course, we strive to make you fully equipped to become a developer who can execute full fledged Data Science projects. By taking this course, you will Appreciate what Data Science really is Understand the Data Science Life Cycle Learn to use R for executing Data Science Projects Master the application of Analytics and Machine Learning techniques Gain insight into how Data Science works through end-to-end use cases. By becoming a student of V2 Maestros, you will also get maximum discounts on all of our other current and future courses (coupon codes inside the course material). You will also get prompt support of all your queries and questions. We continuously strive to improve our course material to reflect the latest trends and technologies What are the requirements? Programming Experience in at least one language like Java, C/C++/C#, Python, Perl Experience in analyzing Data preferred What am I going to get from this course? Over 55 lectures and 11 hours of content! Appreciate what Data Science really is Understand the Data Science Life Cycle Learn to use R for executing Data Science Projects Master the application of Analytics and Machine Learning techniques Gain insight into how Data Science works through end-to-end use cases. What is the target audience? IT Professionals aspiring to be Data Scientists Students who want to learn about Data Science domain Statisticians and Project Managers who want to expand their horizon into Data Science Section 1: Introduction Lecture 1 About this Course 08:12 Lecture 2 About V2 Maestros 01:39 Lecture 3 Resource Bundle Article Section 2: What is Data Science? Lecture 4 Basic Elements of Data Science 11:51 Lecture 5 The Dataset 10:44 Lecture 6 Learning from relationships 12:55 Lecture 7 Modeling and Prediction 09:31 Lecture 8 Use Cases for Data Science 07:47 Section 3: Data Science Life Cycle Lecture 9 Stage 1 - Setup 11:46 Lecture 10 Stage 2 - Data Engineering 11:57 Lecture 11 Stage 3 & 4 - Analysis and Production 19:16 Section 4: Statistics for Data Science Lecture 12 Types of Data 07:29 Lecture 13 Summary Statistics 16:10 Lecture 14 Statistical Distributions 19:05 Lecture 15 Correlations 10:09 Section 5: R Programming Lecture 16 Downloading and Installing R and R Studio Article Lecture 17 R Studio - Walkaround 06:40 Lecture 18 R Language Basics 12:04 Lecture 19 Vectors and Lists 08:51 Lecture 20 Data Frames and Matrices 14:41 Lecture 21 Data Manipulation and I/O Operations 10:30 Lecture 22 Programming and Packages 12:41 Lecture 23 Statistics in R 03:01 Lecture 24 Graphics in R 06:51 Lecture 25 R Code Examples - Variables and Vectors 16:18 Lecture 26 R Code Examples - Data Frames and Matrices 15:05 Lecture 27 R Code Examples - Programming Elements 17:18 Lecture 28 R Code Examples - Statistics and Base Plotting System 17:29 Lecture 29 R Code Examples - ggplot 17:22 Section 6: Data Engineering Lecture 30 Data Acquisition 16:01 Lecture 31 Data Cleansing 10:50 Lecture 32 Data Transformations 11:09 Lecture 33 Text Pre-Processing TF-IDF 14:53 Lecture 34 R Examples for Data Engineering 11:14 Section 7: Machine Learning and Predictive Analysis Lecture 35 Types of Analytics 12:08 Lecture 36 Types of Learning 17:16 Lecture 37 Analyzing Results and Errors 13:46 Lecture 38 Linear Regression 19:00 Lecture 39 R Use Case : Linear Regression 18:01 Lecture 40 Decision Trees 10:42 Lecture 41 R Use Case : Decision Trees 19:36 Lecture 42 Naive Bayes Classification 19:21 Lecture 43 R Use Case : Naive Bayes 19:12 Lecture 44 Random Forests 10:31 Lecture 45 R Use Case : Random Forests 18:47 Lecture 46 K-means Clustering 11:53 Lecture 47 R Use Case : K-Means clustering 16:24 Lecture 48 Association Rules Mining 11:31 Lecture 49 R Use Case : Association Rules Mining 13:11 Section 8: Advanced Topics Lecture 50 Artificial Neural Networks and Support Vector Machines 04:35 Lecture 51 Bagging and Boosting 11:27 Lecture 52 Dimensionality Reduction 07:28 Lecture 53 R Use Case : Advanced Methods 17:18 Section 9: Conclusion Lecture 54 Closing Remarks 03:35 Lecture 55 BONUS Lecture : Other courses you should check out Article | ||||
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