If you are a data science student in 2025, these ten GitHub repositories are valuable to follow. They help you learn core skills, follow a curriculum, practice projects, prepare for interviews, and join a community. Each repository is beginner-friendly, active in recent years, and widely used. The GitHub link is shown below the title so you can star and explore it easily.
1. academic/awesome-datascience
GitHub Repository: academic/awesome-datascience
"Awesome Data Science" is a curated list of learning paths, tutorials, tools, books, and real-world project ideas. It helps you find quality content in one place. It is updated regularly and beginner-friendly with clear sections like "where do I start", "toolbox", and "literature".
Why star it? Because it gives you a roadmap to follow reliable tools and learning resources without confusion.
2. microsoft/Data-Science-For-Beginners
GitHub Repository: microsoft/Data-Science-For-Beginners
This is a structured 10-week curriculum by Microsoft. It includes 20 lessons covering data science basics like data ingestion, cleaning, visualization, statistics, and machine learning. Every lesson has quizzes, instructions, assignments, and solutions.
Why star it? It gives a guided course to build your understanding step by step and is ideal for a student learning by doing.
3. ossu/data-science
GitHub Repository: ossu/data-science
Open Source Society University (OSSU) repository outlines a full university-level curriculum in data science using MOOCs and free online courses. It covers math, statistics, programming, machine learning, and data engineering.
Why star it? It lets you follow a university-style learning path at your own pace with trusted online resources.
4. veb-101/Data-Science-Projects
GitHub Repository: veb-101/Data-Science-Projects
This repo lists hands-on end-to-end data science projects. Each project idea includes resources and direction on implementing real workflows, from data collection to presentation.
Why star it? Practice matters. This repo helps you build real projects and showcase them in your portfolio.
5. yash42828/Data-Science-All-Cheat-Sheet
GitHub Repository: yash42828/Data-Science-All-Cheat-Sheet
A collection of cheat sheets covering Python libraries, machine learning basics, SQL, statistics, and big data. Handy for quick reference before coding or interviews.
Why star it? Sometimes you just need a quick summary to recall a formula or function.
6. awesomedata/awesome-public-datasets
GitHub Repository: awesomedata/awesome-public-datasets
This repo gathers high-quality public datasets sorted by topic and domain. It is helpful for data science practice and building sample projects.
Why star it? You need real data to work on. This repo gives many reliable sources in one place.
7. siboehm/awesome-learn-datascience
GitHub Repository: siboehm/awesome-learn-datascience
A curated list of beginner-friendly tutorials, MOOCs, guides, and books to help you start data science in the right way.
Why star it? It gives you a learning path adjusted to your skill level with high-quality materials.
8. CIS-Team/Data-Science-Roadmap-2022
GitHub Repository: CIS-Team/Data-Science-Roadmap-2022
Also called "Data Science Roadmap 2025", this repo provides a self-learning roadmap. It explains skills you need, tasks sequence, example projects, and tools to explore.
Why star it? Roadmaps help you stay organized and know what to study next.
9. rfordatascience/tidytuesday
GitHub Repository: rfordatascience/tidytuesday
Tidytuesday is a weekly data project challenge organized by the Data Science Learning Community. Each week you get a new dataset and can practice cleaning, analysis, and visualization in R or Python.
Why star it? It builds consistent practice and community feedback, crucial for growth as a student.
10. pandas-dev/pandas
GitHub Repository: pandas-dev/pandas
While not exclusively for data science learners, the official pandas library repository is essential for mastering data manipulation and analysis. Many students star it as they learn the Python data toolkit. From its README, issue trackers, and examples you learn how real data handling works.
Why star it? Pandas is central to data analysis in Python and being familiar with its repo helps you browse code examples and updates.
Why These Repositories Matter in 2025
Data science is a fast-growing field. Having quality sources matters now more than ever. These repositories stand out because:
- They update often and stay relevant in 2025 (Microsoft curriculum, OSSU updates, community lists).
- They provide structured learning (curriculum, roadmap, cheat sheets).
- They support hands-on practice (project lists, Tidytuesday challenges).
- They connect to community feedback and open-source contributors.
- They reduce learning overload by pointing to trusted resources rather than scattering across the web.
How to Use These Repositories (Suggested Plan)
Here is a simple study flow based on these repos, spread over about 12 weeks. Adjust as needed.
Week 1-2: Set Foundations
- Start the Microsoft "Data Science For Beginners" lessons. Do a lesson every other day, complete quizzes and assignments.
- Explore the OSSU "data-science" repo to see the recommended courses and structure.
Week 3-4: Learn Tools and Shortcuts
- Browse the cheat-sheet repo (Data-Science-All-Cheat-Sheet). Download PDF, review key commands in pandas, ML, SQL.
- Begin exploring datasets via "awesome-public-datasets" for projects.
Week 5-6: Build Projects
- Use the "Data-Science-Projects" repo to pick 2-3 project ideas. Try them end-to-end: gather data, clean, model, visualize.
- Keep notes on tools used and lessons learned.
Week 7-8: Follow Roadmaps
- Read through the "awesome-datascience" lists to find tutorials or tools you haven't seen.
- Follow the CIS-Team roadmap to check skill coverage and fill gaps.
Week 9-10: Join Community Challenges
- Start participating in Tidytuesday. Complete one weekly challenge. Post your notebook on GitHub.
- Share results and engage with other learners.
Week 11-12: Review and Prepare
- Use cheat sheets to revise topics.
- Explore pandas repo to see how library functions are built and documented.
- Reflect: write README for your projects, document what you built and what you learned.
Tips for Better Learning
- Star these repos on GitHub to keep future updates visible.
- Clone or fork the Microsoft curriculum and project repo to your own GitHub so you can track your progress.
- Use GitHub issues and discussions to ask questions or report typos - it builds confidence.
- Keep all your own project code in a portfolio repo separate, link to useful repos in your README.
- Practice consistent small tasks: review a cheat sheet topic or dataset weekly.
- Collaborate by submitting small improvements, like fixing README layout or adding an example dataset.
Summary Table
| # | Repository | Why Star It |
|---|---|---|
| 1 | academic/awesome-datascience | Curated learning path, tools, tutorials |
| 2 | microsoft/Data-Science-For-Beginners | 10-week structured curriculum |
| 3 | ossu/data-science | University-style roadmap and resource list |
| 4 | veb-101/Data-Science-Projects | Project ideas to practice end-to-end |
| 5 | yash42828/Data-Science-All-Cheat-Sheet | Handy quick reference sheets |
| 6 | awesomedata/awesome-public-datasets | High-quality datasets for practice |
| 7 | siboehm/awesome-learn-datascience | Beginner tutorials and MOOCs |
| 8 | CIS-Team/Data-Science-Roadmap-2022 | Skill roadmap updated for 2025 |
| 9 | rfordatascience/tidytuesday | Weekly practice with community |
| 10 | pandas-dev/pandas | Master core Python data toolkit |
Final Thoughts
Star these GitHub repositories now - that way you get their updates, community changes, and new resources as data science evolves in 2025. They help you learn fundamentals, practice real projects, follow a curriculum, and stay motivated. You begin with lessons, then move to projects, community challenges, and review with cheat sheets.
This list keeps learning simple and grounded. Follow the plan that feels good for you. Learn by doing. Look at code. Ask questions. Build your GitHub portfolio. In a year or two, you'll be able to work on bigger data science or machine learning tasks with confidence.