README

https://academy.microsoft.com/en-us/tracks/data-science

About

in 2016-2017 I completed this program on edx.org

Overall I thought it was a good program, with some classes more helpful than others. The courses weren't so easy that you could get by without doing some work but were easy enough that even I (the 2017 release of me no less!) could complete them.

The coverage was Microsoft-heavy (who'd a thunk!?) but with Microsoft's recent embracement of Open-Source means that many of the "Microsoft" products are just managed versions of Open-Source Projects.

Contents

In Brief

1. Introduction to Data Science

2. Analyze and Visualize Data with Excel

3. Query Relational Data with Transact-SQL

4. Statistical Thinking for Data Science and Analytics

5. Exploring Data with Code - Intro to Python for Data Science

6. Understand Core Data Science Concepts - Data Science Essentials

7. Understand Machine Learning - Principles of Machine Learning

8. Use Code to Manipulate and Model Data - Python

9. Applied Machine Learning

10. Microsoft Professional Capstore: Data Science

Introduction to Data Science

https://www.edx.org/course/introduction-to-data-science

https://github.com/MicrosoftLearning/Data-Science-Orientation

This course was extremely basic and served as an introduction to the subject and an overview of the the MPP Data Science Track.

Query Relational Data with Transact-SQL

https://courses.edx.org/courses/course-v1:Microsoft+DAT201x+5T2016/course/

https://github.com/MicrosoftLearning/QueryingT-SQL

A great course on T-SQL (the kind of SQL that SQL-Server uses). Takes the user from knowing nothing about SQL to doing all kinds of advanced queries!

Analyze and Visualize Data with Excel

https://courses.edx.org/courses/course-v1:Microsoft+DAT206x+5T2016/course/

https://github.com/MicrosoftLearning/Analyzing-Visualizing-Data-Excel

This course is a great overview of Excel. Excel is not my favorite data-science tool but sets the bar for spreadsheet applications. Excel has some pretty convenient features if you are working on a small data-set.

Statistical Thinking for Data Science and Analytics

https://courses.edx.org/courses/course-v1:ColumbiaX+DS101X+1T2016/course/

In this release of the MPP Data Science track a class from Columbia University was used to cover basic statistics.

Exploring Data with Code - Intro to Python for Data Science

https://courses.edx.org/courses/course-v1:Microsoft+DAT208x+5T2016/course/

Understand Core Data Science Concepts - Data Science Essentials

https://courses.edx.org/courses/course-v1:Microsoft+DAT203.1x+5T2016/course/

https://github.com/MicrosoftLearning/Data-Science-Essentials

Understand Machine Learning - Principles of Machine Learning

https://courses.edx.org/courses/course-v1:Microsoft+DAT203.2x+5T2016/course/

https://github.com/MicrosoftLearning/Principles-of-Machine-Learning-Python

Use Code to Manipulate and Model Data - Python

https://courses.edx.org/courses/course-v1:Microsoft+DAT210x+5T2016/course/

Applied Machine Learning

https://courses.edx.org/courses/course-v1:Microsoft+DAT203.3x+5T2016/course/

Microsoft Professional Capstore: Data Science

https://courses.edx.org/courses/course-v1:Microsoft+DAT102x+5T2016/course/

Getting Started

To get started submit and issue or make a pull request!