Probability & Statistics for Data Scientists

Everything I learned the hard way, across multiple FAANG data science loops, distilled into one book.

I wrote this after sitting in too many stats rounds where I knew the formula but couldn't defend the answer past the second follow-up. Textbooks don't prepare you for that. Blog posts give you fragments. YouTube gives you vibes. Nothing gives you the actual playbook.

This does.

Inside, you'll find the exact toolkit that gets candidates through the stats round at top-tier data science orgs:

1. How to read the shape of a prompt before you reach for the math, the move that separates senior candidates from everyone else
2. Probability fundamentals that hold up under three rounds of pressure: Bayes, conditioning, independence, and the fallacies interviewers know you might commit
3. The six workhorse distributions and how to recognize each one in the wild
4. Hypothesis testing, confidence intervals, and Bayesian inference, what to say, what not to say, and the assumption that could flip the answer
5. Bootstrap, multiple testing, regression, A/B test sizing, and causal inference, the real interview surface
6. Six canonical probability puzzles worked end to end, the way a strong candidate actually walks through them
7. A complete playbook: the four-beat answer shape, the cheat sheet for every chapter, and a two-page formula reference

This is the one resource I wish someone had handed me before my first onsite. Skip the months of scattered prep. Walk in with the framework that works.

If you're interviewing for a data science role at a top company, this is the only stats prep book you need.