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Guide · Open source

How to use Data-Science-For-Beginners: a guided 10-week course

Updated · written with AI from the project's own documentation and checked against our catalogue data

Data-Science-For-Beginners is a free, MIT-licensed curriculum from Microsoft, published on GitHub, that teaches data science to people new to the field. It is organized as 20 lessons over 10 weeks. This guide shows you how to get the materials onto your computer and how to work through a lesson from start to finish.

Data Science for Beginnersmicrosoft/Data-Science-For-Beginners · ★ 37.9k · MIT

Quick start

  1. 1Clone the repository. Run the sparse checkout clone command from the README to download the course without the translation folders.
  2. 2Enter the folder. Change into the Data-Science-For-Beginners directory you just created.
  3. 3Open Week 1. Start with the first lesson so you build the foundations the later weeks depend on.
  4. 4Attempt the pre-lesson quiz. Answer the quiz before reading to see what you already know.
  5. 5Read and try the lesson. Follow the written instructions and work the material yourself.
  6. 6Complete the assignment. Do the project-based assignment, then check your work against the solution.
  7. 7Finish with the post-lesson quiz. Use the post-lesson quiz to confirm what you learned before moving on.

What the course contains

The course is a set of lesson materials rather than a software product, so there is nothing to install beyond getting the files. Each of the 20 lessons includes a pre-lesson quiz, written instructions, a solution, a post-lesson quiz, and an assignment.

The README describes a project-based approach, so you learn by building things rather than mainly watching videos. The materials are written and the main language of the repository is Jupyter Notebook.

  • 20 lessons spread over 10 weeks
  • Pre- and post-lesson quizzes for each lesson
  • Written instructions, a solution and an assignment per lesson
  • Available in English and many translations listed in the README

Download the course with sparse checkout

The repository includes more than 50 translations, which makes the full download large. The README recommends cloning with sparse checkout to skip the translation folders. Run the first command, then move into the folder and run the second.

On Windows, the README gives the same steps using CMD with double quotes around the paths.

git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.git cd Data-Science-For-Beginners && git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'

Work through a lesson

Start with Week 1 and take the lessons in order, since later weeks build on earlier ones. For each lesson, complete the pre-lesson quiz, read the written instructions, and try the work yourself before checking the solution. Then do the assignment and finish with the post-lesson quiz.

The README points to the Installation Guide and Usage Guide in the repository for more detailed setup and examples. Check those files if you get stuck on setup.

  • Take the pre-lesson quiz to see what you already know
  • Read the written instructions and attempt the work
  • Compare with the solution once you have tried
  • Complete the assignment and the post-lesson quiz

Get help and stay current

The README lists a Microsoft Foundry Discord and a Microsoft Foundry Developer Forum, and mentions a Learn with AI series that offered tips on using GitHub Copilot for Data Science. The README also links a Student Hub page and Microsoft Learn Student Ambassadors for students. It says the Student Hub content is switched out at least monthly, so it is worth checking back.

The repository accepts issues and pull requests, as its badges show.

What people use it for

  • Teaching yourself data science from zero with a fixed 10-week schedule
  • Giving a student or class a free, structured coursework option
  • Working through the course in another language from the listed translations
  • Building a portfolio of small projects through the assignments

Frequently asked questions

Is Data-Science-For-Beginners free?

The repository is licensed under MIT, and the README describes it as a free course for beginners. You download it from GitHub and work through the lessons yourself.

What license does Data-Science-For-Beginners use?

The repository uses the MIT license, as shown on its GitHub license badge and LICENSE file.

Do I need prerequisites before starting?

The README does not list any prerequisites. It is written as a beginner curriculum, so you can start with Week 1.

How do I download only the English course without translations?

Use the sparse checkout commands in the README. They clone the repository and exclude the translations and translated_images folders, which makes the download much faster.

Is this course good for experienced data scientists?

No. The README describes it as a beginner curriculum, so experienced practitioners looking for advanced techniques will likely find it too basic.

Are there alternatives to Data-Science-For-Beginners?

Comparable projects include freeCodeCamp, Kaggle Learn, The Data Science Handbook by Jake VanderPlas, and the Python Data Science Handbook. Choose based on whether you want a week-by-week course or reference material.

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Our verdict

Data-Science-For-Beginners suits complete beginners who want a free, self-paced, week-by-week course with hands-on assignments and quizzes. It is not a good fit if you need a live instructor or cohort, want advanced techniques, or expect a software product to install.

Install options & downloads