Math & Statistics

A working grasp of mathematics is what separates someone who runs a model from someone who knows when the model is wrong. These courses cover probability, descriptive and inferential statistics, hypothesis testing and confidence intervals, plus the linear algebra and calculus that underpin machine learning algorithms. They are taught for data practitioners rather than mathematicians, with worked examples in Python or R rather than proofs. Particularly useful if you can already build models but struggle to explain why one performs better than another, or if you need to design and interpret A/B tests that hold up under scrutiny.