- Curricular Guideline
- How to use this guide
- How to contribute
- Code of conduct
This is a path for those of you who want to complete the Data Science undergraduate curriculum on your own time, for free, with courses from the best universities in the World.
In our curriculum, we give preference to MOOC (Massive Open Online Course) style courses because these courses were created with our style of learning in mind.
OSSU Data Science uses the report Curriculum Guidelines for Undergraduate Programs in Data Science as our guide for course recommendation.
Students who already know basic programming in any language can skip this first course
The Algorithms courses are taught in Java. If students need to learn Java, they should take this course first
It is possible to finish within about 2 years if you plan carefully and devote roughly 20 hours/week to your studies. Learners can use this spreadsheet to estimate their end date. Make a copy and input your start date and expected hours per week in the
Timeline sheet. As you work through courses you can enter your actual course completion dates in the Curriculum Data sheet and get updated completion estimates.
Some courses can be taken in parallel, while others must be taken sequentially. All of the courses within a topic should be taken in the order listed in the curriculum. The graph below demonstrates how topics should be ordered.
Now you just need to pass the cards to the
Doing column or
Done column as you progress in your study.
Python and R are heavily used in Data Science community and our courses teach you both. Remember, the important thing for each course is to internalize the core concepts and to be able to use them with whatever tool (programming language) that you wish.
You must share only files that you are allowed. Do NOT disrespect the code of conduct that you sign in the beginning of your courses.
You can open an issue and give us your suggestions as to how we can improve this guide, or what we can do to improve the learning experience.
If you want to suggest a new resource, send a pull request adding such resource to the extras section. The extras section is a place where all of us will be able to submit interesting additional articles, books, courses and specializations.
We have a Discord server! This should be your first stop to talk with other OSSU students. Why don't you introduce yourself right now?
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You can also interact through GitHub issues.