alastairrushworth/free-data-science

What it solves

Learning data science often means paying for courses or giving up personal data to sign up for trial accounts. This project solves that by gathering high-quality, free learning resources that require no sign-up, organized by topic so learners can find what they need quickly without sifting through the web.

How it works

It is a carefully curated list of links to books, courses, tutorials, videos, podcasts, cheat sheets, and communities. Each entry is tagged with an icon showing its format (e.g., 🎓 for course, 📚 for book, ▶️ for video). The material is arranged thematically, starting with R and Python basics, then moving into tools like Polars, shell, Git, SQL, Docker, and data engineering, and finishing with machine learning theory and interpretability. The list is community-maintained: if you spot a broken link or mistake, you can file an issue or submit a pull request.

Who it's for

Anyone wanting to learn data science without paying or signing up. It suits complete beginners starting with R or Python, experienced programmers moving into data science, data engineers looking for pipeline tools, and machine learning practitioners wanting to deepen their understanding. Because all resources are free and sign-up-free, it is ideal for self-learners, students, and professionals on a budget.

Highlights

• Strictly free and sign-up-free resources only.
• Broad coverage: R, Python, Polars, shell, regex, web scraping, Git, Spark, SQL, DuckDB, Docker, publishing tools, data engineering, machine learning, and interpretability.
• Each entry is tagged with a clear icon for its format (course, book, video, etc.).
• Community-driven corrections and additions are welcomed via GitHub issues or pull requests.
• Includes both introductory material and advanced references, such as “R for Data Science” and “Elements of Statistical Learning.”