> 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 ## About in 2016-2017 I completed this program on [edx.org](https://edx.org) [![](/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 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 10. Microsoft Professional Capstore: Data Science ### Introduction to Data Science 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 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 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 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 ### Understand Core Data Science Concepts - Data Science Essentials ### Understand Machine Learning - Principles of Machine Learning ### Use Code to Manipulate and Model Data - Python ### Applied Machine Learning ### Microsoft Professional Capstore: Data Science ## Getting Started To get started submit and issue or make a pull request! --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://mpp-data-science.gitbook.io/project/master.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.