Users Online

· Guests Online: 109

· Members Online: 0

· Total Members: 188
· Newest Member: meenachowdary055

Forum Threads

Newest Threads
No Threads created
Hottest Threads
No Threads created

Latest Articles

· Do while loop
· The for loop in C
· C while loop
· Pointers in C
· Functions in C

Articles Hierarchy

Articles Home » Data Science » Become a Data Scientist

Become a Data Scientist

Become a Data Scientist

 

 

Whether you're working in IT or simply have an interest in entering the exciting field, this learning path will support you in developing a career in data science. Learn about the fundamental stages of data science work, from Statistics and Systems Engineering to Data Mining and Machine Learning.
Build a solid foundational understanding of statistics, which is necessary for any data science-related field.
Discover the many categories of job specialization within Data Science.
Learn how to source, explore, and communicate with data through graphs and statistics.

 

 

 

 

01

Bracketology Club: Using March Madness to Learn Data Science with Brian Tonsoni

12m 7s • COURSE
When one pictures the group who bested over 100 sports experts to win the 2016 Bracket Matrix—an online March Madness bracket competition—a classroom full of small-town high school students might not be the first image that comes to mind. It certainly wasn't what social studies teacher Brian Tonsoni expected when started Delphi Bracketology—a high school club that uses data science to predict which teams the NCAA will select for the Division I Men's Basketball Tournament. But what began as an informal gathering of sports fans soon grew into a collection of champions. In this short film, meet some of the members of this remarkable team, and learn how Tonsoni's informal, project-based approach to learning helped these young bracketologists acquire the kinds of key skills—data science, public speaking, and more—that every teacher hopes to instill in their students. 
02

Data Science & Analytics Career Paths & Certifications: First Steps with Jungwoo Ryoo

1h 12m • COURSE
The career opportunities in data science, big data, and data analytics are growing dramatically. If you're interested in changing career paths, determining the right course of study, or deciding if certification is worth your time, this course is for you.

Jungwoo Ryoo is a professor of information science and technology at Penn State. Here he reviews the history of data science and its subfields, explores the marketplaces for these fields, and reveals the five main skills areas: data mining, machine learning, natural language processing (NLP), statistics, and visualization. This leads to a discussion of the five biggest career opportunities, the six leading industry-recognized certifications available, and the most exciting emerging technologies. Along the way, Jungwoo discusses the importance of ethics and professional development, and provides pointers to online resources for learning more.
Topics include:
  • A history of data science
  • Why data analytics is important
  • How data science is used in fraud detection, disease control, network security, and other fields
  • Data science skills
  • Data science roles
  • Data science certifications
  • The future of data science
03

Data Science Foundations: Fundamentals with Barton Poulson

3h 6m • COURSE
Introduction to Data Science provides a comprehensive overview of modern data science: the practice of obtaining, exploring, modeling, and interpreting data. While most only think of the "big subject," big data, there are many more fields and concepts to explore. Here Barton Poulson explores disciplines such as programming, statistics, mathematics, machine learning, data analysis, visualization, and (yes) big data. He explains why data scientists are now in such demand, and the skills required to succeed in different jobs. He shows how to obtain data from legitimate open-source repositories via web APIs and page scraping, and introduces specific technologies (R, Python, and SQL) and techniques (support vector machines and random forests) for analysis. By the end of the course, you should better understand data science's role in making meaningful insights from the complex and large sets of data all around us.
Topics include:
  • Assess the skills required for a career in data science.
  • Evaluate different sources of data, including metrics and APIs.
  • Explore data through graphs and statistics.
  • Discover how data scientists use programming languages such as R, Python, and SQL.
  • Assess the role of mathematics, such as algebra, in data science.
  • Assess the role of applied statistics, such as confidence intervals, in data science.
  • Assess the role of machine learning, such as artificial neural networks, in data science.
  • Define the components of effective data visualization.
04

Statistics Foundations: 1 with Eddie Davila

2h 6m • COURSE
Statistics is not just the realm of data scientists. All types of jobs use statistics. Statistics are important for making decisions, new discoveries, investments, and predictions. Whether the subject is political races, sports rankings, shopping trends, or healthcare advancements, statistics is an instrument for understanding your favorite topic at a deeper level. With these beginner-level lessons, you too can master the terms, formulas, and techniques needed to perform the most common types of statistics.

Professor Eddie Davila covers statistics basics, like calculating averages, medians, modes, and standard deviations. He shows how to use probability and distribution curves to inform decisions, and how to detect false positives and misleading data. Each concept is covered in simple language, with detailed examples that show how statistics are used in real-world scenarios from the worlds of business, sports, education, entertainment, and more. These techniques will help you understand your data, prove theories, and save time, money, and other valuable resources—all by understanding the numbers.
Topics include:
  • Why statistics matter
  • Evaluating your data sets
  • Finding means, medians, and modes
  • Calculating standard deviation
  • Measuring distribution and relative position
  • Understanding probability and multiple-event probability
  • Describing permutations: the order of things
  • Calculating discrete and continuous probability distributions
05

Learning Data Governance with Jonathan Reichental

41m 4s • COURSE
In the era of big data and data science, most businesses and institutions realize the power of data. Yet far too many fail to appreciate the legal and fiscal responsibilities and liabilities associated with it. The stakes are high, but a well-rounded data governance process can help ensure the consistent quality, availability, integrity, and usability of your data.

Here Dr. Jonathan Reichental explains how to begin to implement a data governance program within any organization. Learn the components of data governance, its strategic value, the roles and responsibilities of stakeholders, and the overall steps that an organization needs to take to manage, monitor, and measure the program. Plus, get guidance on a set of next steps for building skills. As the data science domain grows, so does the demand for data governance expertise. Start here for your first look at this in-demand skill.
Topics include:
  • What is data governance?
  • Why do organizations need data governance?
  • Who owns the data?
  • Designing the data governance process
  • Managing, maintaining, monitoring, and measuring your program
06

Data Science Foundations: Data Mining with Barton Poulson

4h 40m • COURSE
All data science begins with good data. Data mining is a framework for collecting, searching, and filtering raw data in a systematic matter, ensuring you have clean data from the start. It also helps you parse large data sets, and get at the most meaningful, useful information. This course, Data Science Foundations: Data Mining, is designed to provide a solid point of entry to all the tools, techniques, and tactical thinking behind data mining.

Barton Poulson covers data sources and types, the languages and software used in data mining (including R and Python), and specific task-based lessons that help you practice the most common data-mining techniques: text mining, data clustering, association analysis, and more. This course is an absolute necessity for those interested in joining the data science workforce, and for those who need to obtain more experience in data mining.
Topics include:
  • Prerequisites for data mining
  • Data mining using R, Python, Orange, and RapidMiner
  • Data reduction
  • Data clustering
  • Anomaly detection
  • Association analysis
  • Regression analysis
  • Sequence mining
  • Text mining

 
07

Excel 2016: Managing and Analyzing Data with Dennis Taylor

3h • COURSE
Large amounts of data can become unmanageable fast. But with the data management and analysis features in Excel 2016, you can keep the largest spreadsheets under control. In this course, Dennis Taylor shares easy-to-use commands, features, and functions for maintaining large lists of data in Excel. He covers sorting, adding subtotals, filtering, eliminating duplicate data, and using Excel's Advanced Filter feature and specialized database functions to isolate and analyze data. With these techniques, you'll be able to extract the most important information from your data, in the shortest amount of time.
Topics include:
  • Prepping data for analysis
  • Multiple-key sorting
  • Sorting by rows or by columns
  • Setting single- and multi-level subtotals
  • Using text, numeric, and date filters
  • Creating custom filters
  • Filtering tables using slicers
  • Using Advanced Filter
  • Eliminating duplicate data
  • Using SUMIF and COUNTIF functions for quick data analysis
  • Working with the database functions such as DSUM and DMAX
08

Data Visualization: Storytelling with Bill Shander

1h 37m • COURSE
We are wired for story. We crave it. Storytelling has played an integral role in our ability to make progress. It should come as no surprise, then, that presenting data and information in story form maximizes the effectiveness of our communication. We can create deeper emotional responses in our audience when we present data in story form.

Join data visualization expert Bill Shander as he guides you through the process of turning "facts and figures" into "story" to engage and fulfill our human expectation for information. This course is intended for anyone who works with data and has to communicate it to others, whether a researcher, a data analyst, a consultant, a marketer, or a journalist. Bill shows you how to think about, and craft, stories from data by examining many compelling stories in detail.
Topics include:
  • Creating a narrative structure for data
  • Applying narrative to data
  • Identifying what you want to say with the data
  • Analyzing what your data is saying
  • Determining what your audience needs to hear
  • Leveraging tables, charts, and visuals
  • Ensuring your narrative provides context and direction

 

 

 

 

 

 

· Posted by Superadmin on January 22 2019 · 0 Comments · 884 Reads · Print
Page 1 of 66: 1234...66

Comments

No Comments have been Posted.

Post Comment

Please Login to Post a Comment.

Ratings

Rating is available to Members only.

Please login or register to vote.
No Ratings have been Posted.
Render time: 1.07 seconds
10,826,092 unique visits