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
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
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