awesome-datascience
A curated list of links for learning data science, from courses and books to tools and datasets.
- Stars
- 30.3k
- Forks
- 6.7k
- Licence
- MIT
- Language
- —
- Last update
- 5 days ago
- Type
- Curated list
What it is
This is a community-maintained list of links about data science, organized into sections such as courses, books, tools, podcasts, and datasets. It is meant as a starting path for people who want to learn what data science is and what to study. The README is a curated index of other resources rather than a software program.
- Browse the 'Where do I Start?' and training sections to plan a self-study path
- Find free courses, MOOCs, and tutorials linked from the README
- Look up machine learning and deep learning package ecosystems listed in the toolbox section
- Discover datasets and competitions to practice on
Great for
- Beginners who want a guided set of links to start learning data science
- Students looking for free courses, books, and tutorials to explore
- People who want an overview of data science tools such as machine learning and deep learning packages
- Readers who want to find community channels, newsletters, or podcasts on the subject
Not ideal for
- People who need a single tool or program to install and use, since this is only a list of links
- Readers who need verified, up-to-date course or resource quality, because the README does not show how entries are reviewed for currency
- Advanced practitioners looking for in-depth technical material beyond introductory-level pointers
How to get it
- 1
See the README
Clone the repository
git clone https://github.com/academic/awesome-datascience - 2
Run it
- Open the repository's README on GitHub.
- Use the table of contents to jump to a section such as Training Resources.
- Follow links to courses or tutorials that match your current level.
- Return to the Toolbox section when you are ready to pick a specific library.
- 3
Get the most out of it
- Start with the 'What is Data Science?' section to get a sense of the field before choosing resources.
- Use the table of contents to compare free courses, MOOCs, and intensive programs.
- Check the Datasets section to find practice data for projects.
- Read CONTRIBUTING.md if you want to suggest a missing resource.
Downloads
Strengths
- Broad coverage of topics in one place, from career background to specific tools
- Open source under the MIT license and accepts contributions via CONTRIBUTING.md
- Organized into a table of contents that makes it easy to jump to a section
Trade-offs
- It is a list of external links, so the quality and availability of each linked resource depends on those third parties
- The README is truncated in the provided text, so the full set of sections could not be fully checked here
- Some intro content includes sponsor banners and affiliated book deal links, which readers should weigh
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| awesome-datascience (this) | 30.3k | Curated list | beginner | 5 days ago | Beginners who want a guided set of links to start learning data science |
| Data-Science-For-Beginners | 37.9k | Learning resource | beginner | 16 days ago | Complete beginners who want a guided, week-by-week data science course |
| ML-From-Scratch | 33.3k | Learning resource | beginner | 3.1 years ago | Students who want to see how algorithms like regression or neural networks work internally |
| applied-ml | 30.8k | Curated list | beginner | 2.3 years ago | Engineers planning an ML project who want to see how other companies approached a similar problem |
| Made-With-ML | 49.9k | Learning resource | intermediate | 8 months ago | Software engineers who want to learn how ML models get deployed and maintained |
| 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 |
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.