Prerequisite Skills

Data science at it’s core is mostly applied statistics and mathematics. In order to properly use and interpret a lot of code and output you’ll need some background in statistics and probability. Calculus and linear algebra are helpful too. A background in computer science or programming is helpful but not essential, though you will need basic familiarity with file storage, installing programs, etc.

If you have never taken a statistics class before (or if it’s been a while), fear not! The internet is full of helpful guides and resources to get you started.

If you would like to take courses organized into a logical path, the Open Source Society University’s Data Science program is a great resource.

Statistics and Probability Theory

MIT: Intro to Probability

Udemy: Workshop in Probability and Statistics

EdX: Intro Statistics with R - Learn introductory R alongside statistics

EdX: Statistical Thinking for Data Science and Analytics

EdX: Probability and Statistics in Data Science using Python

MIT: Probabilistic Systems Analysis and Applied Probability

MIT: Statistics for Applications

Linear Algebra

EdX: Linear Algebra - Foundations to Frontiers

MIT: Linear Algebra

Other Mathematics

MIT: Mathematics for Computer Science

Computational Thinking and Theoretical Data Science

MIT: Introduction to Computer Science and Programming in Python EdX: Same Course

MIT: Intro to Computational Thinking and Data Science EdX: Same Course