What's in this 301-page book?

201 real data science interview questions from Facebook, Google, Amazon, Netflix, Stripe, Two Sigma, Citadel and more - with step-by-step detailed solutions!

• Questions cover the topics most frequently tested in data interviews: probability, statistics, machine learning, SQL and database design, coding (Python), product analysis, and A / B testing
• Each chapter has a short crash course on key concepts and formulas that need to be reviewed
• Learn how to solve open case study questions that combine product understanding, business insight, and statistical modeling skills, and practice with case interviews from Airbnb, Instagram, and Accenture
• Learn how to get started with data science, with tips on how to build your resume, create crisp portfolio projects, send cool emails for the net, and better tell your story in behavioral interviews

About Kevin Huo

Kevin Huo is currently a Data Scientist at a Hedge Fund and was previously a Data Scientist at Facebook working on Facebook groups. He holds a bachelor's degree in computer science from the University of Pennsylvania and a bachelor's degree in economics from Wharton. In college he did an internship on Facebook, Bloomberg and Wall Street.

About Nick Singh:

Nick Singh began his career as a software engineer on Facebook's growth team and most recently worked at SafeGraph, a geospatial analytics startup. He holds a BS in Systems Engineering with a major in Computer Science from the University of Virginia. In college, he did an internship at Microsoft and Google. His career advice has been read by over 10 million people on LinkedIn.

Ace the data science interview: 201 real interview questions asked by faang, tech startups, & wall street

Warning: This is NOT a good book for learning Data Science or Machine Learning from scratch. However, it's a great resource to quickly address the hottest topics in the short weeks before your upcoming machine learning or data science interview, and to anticipate the types of questions companies are asking. Where the book really shines is with real-life interview questions. For example, there are 40 statistical interview questions from companies like Google and Amazon, and 35 machine learning questions from companies like Spotify and Citadel. On top of that, there are also non-trivial open cases that do a great job of bringing together all the different aspects of real-world scenarios. I highly recommend reading this before your tech interview.

This book not only provides excellent coverage of various very important topics that might come up in a data science interview, but also some very practical advice that would be applicable to technical interviews in general. I also love that in addition to having a refresher on various topics, the book also provides a LOT of practical questions that will come in handy as I prepare for interviews. Overall, I would recommend it to anyone looking to prepare for data science interviews!

Reducing my 4-year degree in Applied Mathematics to the most relevant concepts is no small feat. Fortunately, Matt and Nick did just that in Ace the Data Science Interview! This book cuts through the fluff and brings you the most relevant information to crush your next tech interview. The statistics chapter provides a hands-on overview of everything from MLE hypothesis testing and plenty of practice problems, so you can drop the STATS 400 scores and prepare effectively. I couldn't recommend this book more highly!