Data Science for Beginners
A free 10-week, 20-lesson beginner data science curriculum from Microsoft, with quizzes and assignments.
- Stars
- 37.9k
- Forks
- 7.6k
- Licence
- MIT
- Language
- Jupyter Notebook
- Last update
- 16 days ago
- Type
- Learning resource
What it is
This is a structured course in data science for people who are new to the field. It is organized as 20 lessons spread over 10 weeks, and each lesson includes pre- and post-lesson quizzes, written instructions, a solution, and an assignment. The course uses a project-based approach, so you learn by building things.
- Work through the 20 lessons in order over 10 weeks
- Complete the assignments in each lesson to practice skills
- Use the pre- and post-lesson quizzes to check your understanding
- Use the translated versions if English is not your first language
Great for
- Complete beginners who want a guided, week-by-week data science course
- Students looking for free structured coursework
- Learners who prefer hands-on, project-based lessons over videos
- People who want to work through material in English or one of the many listed translations
Not ideal for
- Experienced data scientists looking for advanced techniques, since the README describes it as a beginner curriculum
- People who need a live instructor or cohort, since the README only describes self-paced written lessons and community channels
- Anyone expecting a software product to install, since this is a set of lesson materials
How to get it
- 1
Clone and build from source
Clone without translations (Bash / macOS / Linux)
git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.gitEnter the folder and set sparse checkout (Bash / macOS / Linux)
cd Data-Science-For-Beginners && git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' - 2
Run it
- Clone the repository, optionally using the sparse checkout commands to skip translations
- Open the README and the Installation Guide linked from it
- Start with the first lesson and complete its pre-lesson quiz
- Work through the lesson instructions and assignment, then take the post-lesson quiz
- 3
Get the most out of it
- Use the Student Hub page linked in the README for beginner resources and free certificate vouchers
- Join the Microsoft Foundry Discord to ask questions and follow the Learn with AI series
- Check the translations table if you want lessons in another language
- Use the Codespaces badge in the README to open the project in GitHub Codespaces without local setup
Downloads
Strengths
- Free and MIT-licensed, so it can be reused and adapted
- Each lesson pairs instruction with quizzes and assignments
- Available in a large number of languages through the listed translations
Trade-offs
- The README gives little detail on prerequisites or the exact topics covered in each lesson
- The full repository is large because of the many translations, though sparse checkout is offered to reduce download size
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| Data-Science-For-Beginners (this) | 37.9k | Learning resource | beginner | 16 days ago | Complete beginners who want a guided, week-by-week data science course |
| PythonDataScienceHandbook | 50.3k | Learning resource | beginner | 2.4 years ago | Students learning NumPy, Pandas, Matplotlib, or Scikit-Learn through hands-on examples |
| ML-For-Beginners | 91.9k | Learning resource | beginner | 5 days ago | Beginners who want a structured, self-paced introduction to machine learning |
| AI-Expert-Roadmap | 31.3k | Learning resource | beginner | 1.1 years ago | Beginners who are unsure what to learn next in AI or data science |
| awesome-datascience | 30.3k | Curated list | beginner | 5 days ago | Beginners who want a guided set of links to start learning data science |
| awesome-bigdata | 14.7k | Curated list | beginner | 6 days ago | Developers looking for an overview of big data tools to evaluate |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.