> For the complete documentation index, see [llms.txt](https://mpp-data-science.gitbook.io/project/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://mpp-data-science.gitbook.io/project/master.md).

# README

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

## About

in 2016-2017 I completed this program on [edx.org](https://edx.org)

&#x20;[![](/files/-LD2zAOYivngM478nL4P)](https://academy.microsoft.com/en-us/certificates/d5684e05-1963-412f-971a-86da6fb49a83)

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&#x20;
5. Exploring Data with Code - Intro to Python for Data Science
6. Understand Core Data Science Concepts - [Data Science Essentials](/project/06-data-science-essentials.md)
7. Understand Machine Learning - [Principles of Machine Learning](/project/07-principles-of-ml.md)
8. Use Code to Manipulate and Model Data - Python
9. Applied Machine Learning&#x20;
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!