The following is a list of online courses and other resources that might be useful preparation for the UBC MDS program. Completion of these courses does not replace the official program prerequisites. Rather, this page is mainly intended for entering students who may wish to reinforce their preparation before the program starts.
Disclaimer: We have not vetted these courses ourselves but rather selected them based on their descriptions seeming appropriate. If you have feedback about them, please let us know.
- Programming for Everybody (Getting Started with Python), from U. Michigan (Coursera)
- DataCamp, a variety of mini-courses such as Intro to Python for Data Science
- Programming Foundations with Python, Udacity
- Python Programming: A Concise Introduction, from Wesleyan U. (Coursera)
- Introduction to Data Science in Python, from U. Michigan (Coursera)
- Principles of Computing (Part 1), from Rice U. (Coursera)
- R Programming, from Johns Hopkins U. (Courera)
- DataCamp, a variety of mini-courses such as Introduction to R
- Introduction to R for Data Science, from Microsoft (edX)
- Explore Statistics with R, from Karolinska Institutet (edX)
Statistics and Probability
- Introduction to Statistics: Probability, from U. C. Berkeley (via edX)
- Fat Chance: Probability from the Ground Up, from Harvard U. (edX)
- Introduction to Probability and Data with R, from Duke U. (Coursera)
- Basic Statistics, from University of Amsterdam (Coursera)
- Probability and Statistics, from Stanford U.
- Essence of calculus (YouTube series)
- Calculus One, from The Ohio State University (Coursera)
- Calculus: Single Variable, Part 1, Part 2, Part 3 from U. Pennsylvania (Coursera)
- Calculus 1, Differentiation and Integration from MIT (edX)
- Essence of linear algebra (YouTube series)
- Immersive linear algebra, interactive e-book
- Introduction to Linear Models and Matrix Algebra with R, from Harvard U. (edX)
- Linear Algebra Refresher Course with Python (Udacity).
- Applications of Linear Algebra Part 1, from Davidson College (edX)
- A variety of free O’Reilly Ebooks on data science