Master probability theory, combinatorics, and Python simulation for quant interview success -- solve problems analytically, verify with code, and communicate your reasoning under pressure.
Probability Foundations Reset -- Rebuild core probability at interview speed: sample spaces, conditional probability, Bayes' theorem, and structured solution frameworks Discrete Distributions and Counting -- Master combinatorics and discrete distributions (binomial, Poisson, geometric) at interview depth with Python verification Continuous Distributions and Monte Carlo -- Continuous distributions (normal, exponential, uniform) and Monte Carlo simulation as a verification and problem-solving tool Expected Value, Variance, and Decisions -- Multi-step expected value, conditional expectation, variance decomposition, and the decision-making math behind trading games Classic Probability Puzzles and Brainteasers -- Reusable frameworks for interview puzzles: conditioning, recursion, symmetry, indicator variables, and coupling arguments Markov Chains and Interview Problems -- Markov chains, random walks, absorption probabilities, and first passage times at interview depth
A personal probability interview toolkit: structured solution frameworks for every major probability question type, Python Monte Carlo verification for every core concept, and practiced communication technique for explaining reasoning under pressure.
