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

by: Nick Singh (0)

WARNING FOR US + CANADIAN CUSTOMERS: DO NOT BUY USED. ONLY BUY NEW, AND MAKE SURE IT SAYS "SHIPS FROM AMAZON.COM" AND "SOLD BY AMAZON.COM". COUNTERFEIT & pirated books filled with mathematical typos, missing SQL questions, and poor print quality are being sold by 3rd-party Amazon sellers as "NEW" OR "USED - LIKE NEW" or "USED - VERY GOOD" condition books. Do NOT buy used from 3rd party Amazon sellers like 'Morgans' Finds", 'Perez deals', 'Bella.BUG', "HPB Inc" - even if they have a ton of ratings. To ensure a genuine copy, BUY THE BOOK NEW and make sure it says "Ships From & Sold by" (this is usually the default 'New' buying option, but double check please!).

Authored by two Ex-Facebook employees,
Ace the Data Science Interview is the best way to prepare for Data Science, Data Analyst, and Machine Learning interviews, so that you can land your dream job at FAANG, tech startups, or Wall Street.

What's inside this 301 page book?

  • 201 real Data Science interview questions asked by Facebook, Google, Amazon, Netflix, Two Sigma, Citadel and more — with detailed step-by-step solutions!
  • Learn how to break into Data Science, with tips on crafting your resume, creating kick-ass portfolio projects, sending networking cold emails, and better telling your story during behavioral interviews
  • Questions cover the most frequently-tested topics in data interviews: Probability, Statistics, Machine Learning, SQL & Database Design, Coding (Python), Product Analytics, and A/B Testing
  • Each chapter has a brief crash-course on the most important concepts and formulas to review
  • Learn how to solve open-ended case study questions that combine product-sense, business intuition, and statistical modeling skills, and practice with case interviews from Airbnb, Instagram, & Accenture

Praise for Ace the Data Science Interview:

"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 Huo:

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 Singh:
Nick Singh previously worked on
Facebook’s Growth Team and at SafeGraph, a geospatial analytics startup. Currently, he runs SQL interview platform and shares career tips on LinkedIn to his 90,000+ 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.

The Reviews

The chapter on portfolio projects gave amazing tips that helped me create the perfect project that I could add to my resume and talk about in interviews! The chapter on SQL was great in helping me go over the basics and the questions helped me review for my upcoming data analyst interview. Would highly recommend for anyone looking for data related career advice!

I like the way topics are organized. It covers almost everything. This book suffers from two major issues: 1- they try to explain some technical concepts as brief as possible but they couldn’t and majority of the technical parts are half backed especially in statistics and machine learning. 2- it looks the authors didn’t have a chance to read whatever they wrote. The text has many grammatical errors, missing mathematical symbols and many other issues.

Concepts are solutions in this book are very briefly explained which are hard to understand and need to google the solution of the problems. List of questions are good though. Some people said in reviews that they got the job using this book 1-2 days of release of this book. How’s that possible ?

Warning: This is NOT a good book to learn Data Science or ML from scratch. However, it IS a great resource to quickly review the most relevant topics in the few short weeks before an upcoming Machine Learning or Data Science interview, and also anticipate the types of questions companies ask. Where the book really shines is the real interview questions. For example, there are 40 statistics interview questions from companies like Google and Amazon, and 35 Machine Learning questions from companies like Spotify and Citadel. Plus, there are non-trivial open-ended case questions too, which do a great job of combining all the various facets of real-world scenarios. Highly recommend this read before a technical interview.

This book provides not only a great coverage on various really important topics that could surface in a data science interview, but also really practical tips that would be applicable for technical interviews in general. I also love that in addition to getting a refresher on various topics, the book also provides a LOT of practice questions that will come in handy when I prepare for interviews. Overall, would recommend to anyone looking to prepare for data science interviews!

This book is the perfect resource for those who have recently completed, or who are finishing up, a data science-related academic program, boot camp, or self-directed upskilling regimen.It's also a great guide for those with a few years of experience who are ready to start interviewing for more senior DS/ML roles.In short, if you have upcoming interviews in the datasphere, you should treat this book like oxygen...because it's what you need to survive and thrive where you're going!!!The first four chapters deal with basics that you'll need to nail in order to captivate the attention of recruiters & hiring managers (e.g., nailing your resume, nailing your project portfolio, etc.).The next 7 chapters will set you to work grappling with specific interview questions from a range of "holy grail" employers about probability, statistics, ML, SQL & DB design, coding, product sense, & case studies.Example Question: "Uber - Explain the Central Limit Theorem. Why is it useful?"👆 The authors go on to provide a thorough solution (in this case multiple paragraphs that take up ~half of a page).Notably, all 201 interview questions covered in the book include thorough solutions (hence the 290 pages!).BOTTOM LINE: If you want to be competitive in data science interviews at FAANG-level companies - or utterly dominate data science interviews at average companies - then, this book should be your training ground of choice!

I am using the book to prepare for interviews. I found it very useful. I have read the ML chapter so far. i think one needs to have a good foundational knowledge for the topic to use the book more effectively. I did notice some typos but it is very minor compared to the great usefulness of the book. Highly recommend it!

I've just started reading this, and the first few chapters already have lots of interesting advice that I never would have thought of myself. In addition to specific interview question/solutions on statistics, ML, SQL and coding, there are a good amount of general interview/job hunting tips as well. I'm excited to see what else I can take away from this!

Let me start by stating simply that Nick and Kevin have produced a wonderful book. First and foremost, it does an excellent job of teaching you how to make contact to get yourself in the door. Without a plan to do this, your resume is floating in a see of resumes from other hopefuls. This book shows you exactly how to succeed on the step that matters the most. If the book ended there, it would be worth the purchase, but it doesn't. The authors take the time to walk your through a solid core of the tools you will need to be able to exhibit; probability, statistics, machine learning, common database query tools (SQL) and database design, coding, products sense, and some case studies. Each with enough information to put you in control when it is time for the interview. Lastly, though it is likely the last thing people are looking for after years of education, they provide a large number of relevant exercises to cement what you have encountered throughout the book. I can't think of a more accessible book to help someone break into the world of data science. Oh, and if you have the time, reach out to the authors on LinkedIn for additional content and opportunities to make yourself valuable to the data science world.

Helped me revamp my resume and, think through and set up my portfolio. The cold email tips were great and using these, I was able to hear back from recruiters for the first time. Also, the practice questions and solutions are awesome. The content division is apt and I am glad to have a structure for preparing for data science technical interviews. All in all, an awesome book!

"Ace the Data Science Interview" covers most of what you need to know for landing that initial phone screening and doing well in both the behavioral and technical interviews.With such a complex and all-encompassing interview process that is unfortunately becoming more standardized by FAANG and other big tech companies, this book really helps in digesting what to expect and how to answer various number of questions that may come your way.As for the technical, the book divides the technical chapters into 7 categories (which have some overlap) of Probability, Statistics, Machine Learning, SQL & DB Design, Coding, Product Sense, and Case Studies. While the book does cover the most important concepts of these topics, you should absolutely have a decent-to-strong knowledge of them beforehand (with the exception of product sense and case studies). The true value of the technical part of this book comes through from the sample questions and samples answers exemplifying good answers to realistic questions. Beyond the technical, I also gained a lot of value from their advice on how to prepare the behavioral interview.The book also encourages and guides you to go above and beyond for your interviews which I wouldn't recommend for a job prospect you are not super excited about but is worth it for ones you are. For example, I took the advice of learning as much about my interviewers beforehand in order to ask them personally-tailored questions which allowed for a more friendly ambiance.There are some cons such as typos and theory portion of the machine learning chapter was not so useful. It seems like they were in a bit of a rush to publish this book but honestly you shouldn't be coming to this book to learn Machine Learning and if you can look past the typos this book has great value for a data scientist looking to launch his/her career.

Overall the book hits a lot of topics that are brought up in DS interviews. They break down the subjects and the questions very clearly. As mentioned in other reviews, it's not a book to learn these DS concepts but to focus your studying specifically for the interview.The practice problems are very challenging, but there are solutions included.

"Ace the Data Science Interview" is the only book you need to prep for data science interviews.Data science is a fast moving field and while the emergence of academic data science programs may mean that candidates have at least some exposure to the diverse skill set required, everyone has gaps in their knowledge.While prospective data scientists may have a strong grasp of the subject matter knowledge required, interviewing is a skill. This book will help you prepare for technical and behavioral interviews.This book contains great advice how how to network, write cold emails, and get the attention of hiring managers, but the real value here is the sample interview questions, which range from easy to quite difficult.If you're trying to break into data science, or if you're an experienced data scientist looking for a new challenge, this book will help you use your time effectively when prepping for interviews.

The world of data science is vast and it is so easy to get lost. You don't need to learn everything to ace the interviews, there is always a pattern. This book clearly captures that pattern and points you to focus on the most important topics which will ultimately land you a job in this field. The book feels like a conversation between a mentor and a student and is enjoyable. The author forms a connection with the reader using his unique writing style and makes the complex topics easy by chunking and breaking them into small parts that are much easy to consume. I definitely recommend this book.

This book is amazing. I wish I'd known about this before. This is helpful not only for interviews but also a comprehensive way to understand what data scientists do in large companies. I immediately recommend this to my friend to get ready for her data science interview.

Summaries for each chapter are laconic and clear, good set of questions and useful tips inside solutions. Overall feeling that I’m on the interview site but no panic, just a nice conversation :)

This book helped me land a data analyst position. The sections that were very useful for me were the SQL and Product Sense sections. The strategies from the beginning of the book about how to structure your resume and reach out to recruiters definitely helped me get my foot in the door. There are more advanced concepts in this book that I will definitely return to as I progress through my career.

Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street
⭐ 4.5 💛 584
paperback: $28.68
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