Analysis updated 2026-05-18
Build a self-paced Data Science education plan without paying for bootcamps or university tuition.
Track your progress through a structured curriculum by forking the repo and checking off completed courses.
Learn foundational math, programming, and statistics needed for machine learning and data analysis roles.
Join a community of self-taught learners for support and accountability while working through the curriculum.
| ossu/data-science | rasahq/rasa | android/nowinandroid | |
|---|---|---|---|
| Stars | 21,155 | 21,153 | 21,151 |
| Language | — | Python | Kotlin |
| Setup difficulty | easy | hard | moderate |
| Complexity | 2/5 | 4/5 | 3/5 |
| Audience | vibe coder | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
The Open Source Society University Data Science repository is a curated study path that walks you through the equivalent of an undergraduate data-science degree for free, using open courses from well-known universities and online platforms. It is not software you install or run, it is a structured reading list. The maintainers gather the best Massive Open Online Courses they can find, order them, and present the result as a curriculum you can follow on your own time. The structure follows the kind of progression a real degree would: an introduction to data science, an introduction to computer science and programming, then data structures and algorithms, databases, single-variable, linear-algebra and multivariable calculus, statistics and probability, data-science tools and methods, machine learning and data mining, and finally a project. The README explains how to use it: you can finish in about two years studying roughly twenty hours a week, you fork the repository on GitHub and tick items off as you go to track your progress, and a spreadsheet is provided so you can estimate your end date based on your own pace. The curriculum recommends that students already have high-school math and basic statistics before starting, and notes that Python and R are the two main programming languages used along the way, with Java introduced for the algorithms section. The project follows a published report titled "Curriculum Guidelines for Undergraduate Programs in Data Science" as its guide for choosing courses. Someone would use this if they want a serious self-taught path into data science without paying for a degree, and prefer a roadmap to chasing random tutorials. There is a Discord server and GitHub issues for talking with other learners.
Free, self-taught Data Science curriculum covering math, programming, databases, statistics, and machine learning through university courses and MOOCs.
License could not be detected automatically. Check the repository's LICENSE file before use.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly vibe coder.
This repo across BitVibe Labs
Verify against the repo before relying on details.