Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street

by Kevin Huo, Nick Singh

"The advice in this book directly helped me land my dream job" — Advitya Gemawat, ML Engineer, Microsoft “An invaluable resource for the Data Science & ML community” — Aishwarya Srinivasan, Senior Data Scientist, Google "Super helpful career advice on breaking into data & landing your first job in the field" — Prithika Hariharan, President of Waterloo Data Science Club; Data Science Intern, Wish “FINALLY! Cracking the Coding Interview but for Data Science & ML!” — Jack Morris, AI Resident, Google “Solving the 201 interview questions is helpful for people in ALL industries, not just tech!” — Lars Hulstaert, Senior Data Scientist, Johnson & Johnson “The authors explain exactly what hiring managers look for — a must read for any data job seeker” — Michelle Scarbrough, Former Data Analytics Manager, F500 Co.


About Kevin Kevin Huo is currently a Data Scientist at a Hedge Fund , and previously was a Data Scientist at Facebook working on Facebook Groups. He holds a degree in Computer Science from the University of Pennsylvania and a degree in Business from Wharton. In college he interned at Facebook , Bloomberg , and on Wall Street .

About Nick
Nick Singh previously worked on Facebook’s Growth Team and at SafeGraph, a geospatial analytics startup. Currently, he runs SQL interview platform DataLemur.com and shares career tips on LinkedIn to his 120,000+ followers. Nick holds a degree in System Engineering with a minor in Computer Science from the University of Virginia. In college, he interned at Microsoft and at Google’s Nest Labs on the Data Infrastructure Team.

Genres: Computer Science, Programming, Technology, Technical, Textbooks, Artificial Intelligence, Nonfiction

301 pages, Paperback

Published August 16, 2021

Reviews

[Walter Ullon · Rating 5 out of 5]

First, let's get one thing out of the way: Data Science is tricky. It's translating business questions, requirements, and needs into actionable insights. It's designing and interpreting the result of data-driven experiments. It's machine-learning and A.I. It's statistics. It's math. It's everywhere, and it's hard.

While there are no shortage of books out there that seek to aid the prospective product manager or software developer in preparing for interviews in their respective fields, this is the only book in its class for data scientists that covers what you'd need in terms of:
1. behavioral interview preparation
2. probability
3. statistics
4. coding and databases
5. machine learning
6. product sense
7. use cases

Nick and Kevin deserve a lot of praise because a lot of the material in the book would be totally inaccessible to candidates without any experience in some tech/social media. In this book, you'll find contextualized (and some not so) practice questions for FAANG companies as well as finance, and Wall Street. The material is invaluable for this alone.

If I could make a recommendation based on my interview journey thus far, it would be to include material that deals with the shapes of real-world distributions i.e. "what do you think the distribution of time spent per day on Facebook looks like?”

Overall, top-notch, highest possible recommendation!

[Nati S · Rating 4 out of 5]

This book helped me land a few offers. The exercises are helpful and it is written well.

[Duncan McKinnon · Rating 3 out of 5]

The chapter covering databases is very brief and doesn't get into the details of how data scientists/engineers use the tools or how the skills are tested. Also, the choice of postgres syntax seemed arbitrary, as mySQL varieties are more common.

The mathematics sections were more thorough but in my experience less relevant in interviews (unless you're doing research). Overall the questions were good and worth the time to solve, but the choice of material did not reflect my experience either in interviewing data science candidates or in being interviewed for data science and ml engineering roles. The focus in interviews I've seen is most often on experiment design, SQL, and software engineering, which were less of a focus in this book than the probability/stats/ml math sections.

[Ossian Hempel · Rating 5 out of 5]

Excellent resource containing all the essential stuff in one place, in a well-structured manner. The content itself will take forever to master!

[Daeus · Rating 5 out of 5]

Excellent overview of DS interviews. The content sections are moreso refreshers on some subject matter and some are more in depth than others, but the real value of the book is the practice problems + answers and the subject breakdown/general tips of what an interviewer is asking. The writing tiself 3-4 stars (e.g. some links don't work/feels like watching an ad in a book with links, etc at times), but value is 5 stars given it's pretty unique and relevant value for a DS with some experience.

[Steven Allen · Rating 4 out of 5]

I picked up this book from my local awesome library. I am currently in a class learning data science, and picked up this book to see if I might learn something. Most of this book is way above my head, math and computer programming wise. I barely passed college algebra, so a lot of the math formulas and programming is above me.

[Adam · Rating 5 out of 5]

I liked it. Covers math, stats, ML, SQL, coding and product sense. Adds some practical advice about interviewing. The field is moving fast so candidates should also expect questions about the latest developments but understandably that's hard to cover in a printed book.

[Shruti Pandey · Rating 4 out of 5]

A good book to read and revise before interviews but the information is quite condensed, you need to refer to many different other sources. However, I love how convenient revising before interviews becomes, with this book.

[Aditya Kulthe · Rating 4 out of 5]

Good book

[Anil Patel · Rating 5 out of 5]

Good

[Mikhail Filatov · Rating 2 out of 5]

Some chapters, especially in the beginning (how to get an interview) and end (e2e cases) are good, but too much of self-promo from two guys with quite limited career experience and growth.

[Leonardo Vanzei · Rating 5 out of 5]

Good interview guidances

[Sourish Biswas · Rating 5 out of 5]

amazing and so well structured material i have read in the data science community so far. Kudos to the authors

[Kyle Nguyen · Rating 5 out of 5]

Great book to prep for interview!! Some proof solutions in chap 5 and 6 need revisions though, for typo, confusing explanation and wrong formula derived.

[Mark · Rating 5 out of 5]

Solid for folks who need a brush up on interview topics that they don't deal with on a daily basis in their current role.