Learn Quant Interview Concepts

Probability is the foundation of most quantitative interviews. This section teaches how to model uncertainty, condition on information, reason using symmetry, and recognize common probability structures.

7 lessons

Quant interviews often disguise counting problems as probability questions. The key skill is recognizing what is being counted and whether order matters.

8 lessons

Expected-value problems are central to quant interviews because they test modeling and decomposition rather than arithmetic.

8 lessons

Statistics questions test whether you can summarize data, estimate parameters, and avoid fooling yourself with noise — the daily work of a quant researcher.

8 lessons

A small set of distributions appears again and again. Knowing their parameters, moments and defining properties lets you recognize them instantly.

6 lessons

Stochastic processes model how randomness evolves through time: random walks, Markov chains, Poisson arrivals and Brownian motion.

8 lessons

Linear algebra and optimization underpin portfolio construction, factor models and machine learning.

10 lessons

Pricing by no-arbitrage, the Greeks, fixed-income sensitivities and portfolio risk — the finance vocabulary interviewers expect you to use fluently.

10 lessons

How quotes, spreads, information and inventory interact, and how to size and evaluate bets.

8 lessons

The algorithmic toolkit behind quant-developer interviews: complexity, core data structures, search, sorting, dynamic programming and randomized algorithms.

7 lessons