Probability & statistics for quants
The complete theory course — probability and statistics built from first principles, with intuition as the goal: definitions, theorems with proofs or proof sketches, worked examples, simulation code, and prep questions that test whether each idea actually landed. Separate from the applied tutorial series; this is the mathematical bedrock underneath it.
Quantitative work leans on the same core repeatedly: counting outcomes cleanly, conditioning without fumbling Bayes, recognising a distribution from its behaviour, bounding a tail, taking an expectation the fast way ( and its friends), setting up a martingale, and saying — precisely — what a p-value is and is not. Each chapter builds the theory first, makes it visual and simulable, then closes with practice problems that probe that exact spot.
Learning the subject:work Parts I–IV in order (probability is cumulative — Part II especially is where the intuition gets built), then treat Parts V–VI as a menu. Every chapter ends with a Practice problems section — attempt those cold before reading the solutions; the struggle is where the understanding forms. As a reference: each chapter is self-contained with cross-links, and the search bar (⌘K) indexes every topic.