ace the data science interview book review

The 9 Best Data Science Interview Books For 2024

Amidst the wave of tech layoffs, the Data Science & ML job market is looking very competitive. If you're serious about landing a Data Science job in 2024, the 9 books in this article will help you crack the Data Science interview. While we wrote one of the books on the list ( Ace the Data Science Interview ), we admit our book isn't the perfect resource for every type of Data Scientist interview, and hence curated this list of books. Depending on the specialized data role you're interviewing for, you'll likely need to read multiple of these books.

#1 – Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

Data scientists need to understand how their data is being ingested, stored, and processed, especially at smaller companies and startups where their work ties in more closely with Data Infrastructure projects. That's why for hybrid Data Science and Data Engineering roles, you might get asked some system design interview questions, for which the book Designing Data-Intensive Applications will be your savior. Even if there isn't an explicit System Design interview round, Data Scientists might be tested on their knowledge of how to build, deploy, and maintain Machine Learning models and let's face it – to understand DataOps and MLOps well you'll need to first understand how large-scale Software Systems work in general.

In Designing Data-Intensive Applications, Martin Kleppmann covers the main System Design interview topics a Data Scientist might get asked, like data modeling, data storage, distributed systems, and data processing pipelines. The most valuable skill Data Scientists will learn from this book is the ability to juggle the trade-offs involved in designing data-intensive applications, including how to balance the competing goals of performance, scalability, and reliability.

#2 – Cracking the Coding Interview: 189 Programming Questions and Solutions

Cracking the Coding Interview , also lovingly known as the "big fat green book" by Software Engineers, is one the most famous resources for coding interview prep. Data scientists should read Cracking the Coding Interview because most Data Science and ML interviews at companies like Google and Amazon will have a heavy emphasis on Python Data Structures and Algorithms interview questions . Often, these tricky coding questions are asked in the first round or two, along with SQL interview questions , via automated coding assessment tools. If you don't want to get weeded out early in the technical Data Science interview process, reading this book will pay dividends!

While our book Ace the Data Science Interview does have a coding chapter with 30 programming interview questions, Data Scientists who want to go deeper into Computer Science topics like LinkedLists, Graphs, and Trees should give Cracking the Coding Interview a read as well.

#3 – Chip Huyen's Free Machine Learning Interviews Book

Chip Huyen's a former Lecturer at Stanford University where she taught CS 329S: Machine Learning Systems Design , and has built ML tools at NVIDIA, Snorkel AI, and Netflix. Her eBook on ML Interviews is a great free resource for Data Scientists who are applying to roles that involve lots of Machine Learning, because *surprise surprise* those interviews will have lots of ML questions in them! 

The first half of the ML interviews book summarizes the machine learning interview process, what different ML positions are out there, and what kinds of technical ML questions get asked. My favorite part of the book is where Chip draws from her past experience as a ML Hiring Manager and explains the interviewers’ mindset and what kind of signals they look for.

The second half of the ML Interviews book has over 200 ML interview questions, but sadly, most of the questions don't have a solution. If you are looking for a similar list ML interview questions with solutions check out these 30+ ML Interview Questions and the 50+ Machine Learning Questions on DataLemur .

ML Interviews in 2024

With the rise of chatGPT in 2024, folks often wonder if they'll face many questions about transformer models and other LLM concepts during the interview process. Some even think the 2024 data/ML interview expectations have completely changed.

Here's my opinion on ML interviews in 2024: Yes, LLMs are amazing, yes LLMs are hot right now, and YES they do show up in job descriptions. But in 2024 many jobs aren't actually needing in-depth experience with LLMs. They add LLM to the job description to seem sexy, because every exec is asking their team "what's our AI strategy" and every competitor claims to infuse AI into their products.

As such, I think you don't need to over-index on the latest-and-greatest in ML to be able to crush 99% of Data Science and ML interviews in 2024.

#4 – Designing Machine Learning Systems: An Iterative Process for Production-Ready Application

The book Designing Machine Learning Systems by Chip Huyen is like if her free eBook " ML Interviews " and the book "Designing Data-Intensive Applications" had a baby! For Data Scientists interviewing for ML-Heavy positions, or for Data Scientists who'll be working closely with Machine Learning Engineers (MLEs), you'll often be asked open-ended ML interview questions about:

• How to choose the right ML metrics that map to solving a business problem
• How to automate the process for evaluating, deploying, and updating models
• How to work with ML Engineers and DevOps to detect issues in your deployed models
• How to architecting an ML platform that flexibly serves multiple business use cases

Designing Machine Learning Systems exactly covers the answers to these frequently asked open-ended ML interview questions, and is absolutely worth a read for any Data Scientist who'll have to go DEEP into the field of Machine Learning Engineering.

Want Data Science Interivew Tips in Your Inbox?

If you like these Data Science interview recommendations, you are 96.7% encouraged to join our free 9-day Data Science Interview email crash course. We'll email snippets of our Amazon #1 Best Seller, Ace the Data Science Interview , as well as send real FAANG & Wall Street interview questions from our 301 page book.

#5 – Case in Point: Complete Case Interview Preparation

While Case In Point is often hailed by MBA students as the Management Consulting Interview Bible, Data Scientists can stand to gain a ton from this Case Interview prep book because it sharpens core data interview skills such as problem-solving, communication, and business strategy. Plus, it never hurts to sharpen your business skills – while technical skills are the main focus for Data Science interviews, showing a sharp business acumen is a sold way to stand-out from other Data Scientists, especially for open-ended take-home SQL challenges .

Reading Case in Point is especially worthwhile for Data Scientists who're interviewing for positions on Business Analytics and Product Analytics teams. For these kinds of interdisciplinary roles, you'll often be asked data case study interviews, for which the structured problem-solving techniques from Case in Point will come in very handy.

#6 – Cracking the PM Interview

While Cracking the PM Interview is the bible for aspiring Product Managers, Data Scientists shouldn't let the name of the book stop them from picking this up to read. Product-Sense interviews are common for Data Science roles at companies like Facebook and Amazon, where you'll be asked questions like "How would you troubleshoot a drop in time-spent on Instagram" or "What metrics would you use to define the success of Amazon Alexa". To approach these open-ended questions, you'll want to apply the frameworks covered in Cracking the PM interview.

Even if you aren't actively interviewing, I still think this book is well-worth a read as a Product Management 101 crash course. At many tech companies, Senior PMs are the mini-CEOs of a particular organization or product-line, and as such, being able to work with them effectively becomes a crucial skill. By having a taste of what PM'ing looks like from this book, Data Scientists will be able to have a more productive relationship with PMs!

#7 – Trustworthy Online Controlled Experiments

Trustworthy Online Controlled Experiments was written by 3 Data Scientists and Engineers who helped build the large-scale experimentation frameworks at Google, LinkedIn, and Microsoft. Data Scientists who are interviewing for A/B Testing and Experimentation teams should make this book required reading.

To get a tangible sense of the types of A/B testing Data Science interview questions this book can help you answer, check out this list of 40 Probability & Statistics interview questions – you'll see multiple experimentation interview questions from Product Data Science interviews at Airbnb, Facebook, and Uber.

#8 – Build a Career in Data Science

Build a Career in Data Science features tons of career advice on breaking into the Data Science industry, and what it takes to get ahead once you have a job. The book covers portfolio projects, resumes, and how to negotiate an offer once you ace the interview. While this book only has a small appendix with 20 Data Science interview questions, each solution is incredibly detailed.

Our favorite part of Build a Career in Data Science is that they have many outside Data Science voices incorporated into the text. While Emily Robinson (Senior Data Scientist at Warby Parker) and Jacqueline Nolis (Principal Data Scientist at Fanatics) have a wealth of experience, they feature advice from multiple other technical recruiters and Data Science hiring managers which gives this book a well-rounded feel.

If you'd rather listen than read, many of the same insights can be found on the Build a Career in Data Science (The Podcast) .

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

Last but not least, the best overall Data Science Interview book is Ace the Data Science Interview , but don't take our word for it – just read any of the 690+ 5-reviews on Amazon from Data Scientists who used this book to land their dream job at companies like Facebook, Coinbase, and Airbnb.

Ace the Data Science Interview features 201+ real Data Science interviews questions and solutions on topics like Probability, Statistics, Machine Learning, Coding, SQL, and Product-Sense. While the book doesn't cover ML in as comprehensively as Intro to Statistical Learning or dive into Product Management as well as Cracking the PM interview , it offers the most efficient way to practice for Data Science interviews.

To preview the content from the book for free, practice some of the same Data Science Interview Questions on DataLemur or check-out the free 9-day Data Interview crash course. You can also find many of the SQL interview tips in the Ultimate SQL Interview Guide .

In Conclusion: The 9 Best Data Science Interview Books

In conclusion, the 9-best Data Science Interview books you need to read to ace your next Data Science or Machine Learning Interview are:

• Designing Data-Intensive Applications
• Cracking the Coding Interview
• Machine Learning Interviews Book
• Designing Machine Learning Systems
• Case in Point: Complete Case Interview Preparation
• Cracking the PM Interview
• Trustworthy Online Controlled Experiments
• Build a Career in Data Science
• Ace the Data Science Interview

Want more Data Science Book Recommendations? 

If you want more Data Science book recommendations, checkout these 13 must-read Data Science books. While you'll see a few familiar books from this list, we also detail some of the best books to learn Data Science from scratch, and the best books to level-up your Machine Learning knowledge.

Want Data Analytics Book Suggestions?

Looking for the best Data Analytics books, that cover how to learn Data Analytics, and level-up in skills like Statistics and SQL? Check out our 17 best Data Analytics books article also written by yours truly!

We also cover the 3 best SQL books for SQL interviews inside our 5,000 word SQL Interview Guide for Data Analysts .

The Best Data Science Interview Books (Updated for 2024)

There’s been a boom in data science interview books over the last few years as publishers fight to establish the latest go-to data interview guide.

Data science interview books typically include insights into the conduction of interviews and have practice interview questions (with solutions), interviewing strategies, and tips. However, one thing to remember is that prep books aren’t the end-all-be-all, and there isn’t a guide that will replace the need to do mock interviews or practice various SQL, Python, and statistics questions.

The best prep books, though, will demystify the interview process and provide a high-level overview of how to answer questions. After reviewing a variety of books, these are the best data interview books for 2022:

1. Cracking the Data Science Interview

2. Heard in Data Science Interviews

3. 120 Data Science Interview Questions

4. Becoming a Data Head

5. The Data Science Handbook

6. Be the Outlier

7. Data Science Interviews Exposed

8. Ace the Data Science Interview

Cracking the Data Science Interview

Several authors have taken the best-selling Cracking the PM Interview formula and applied it to just about every field. Cracking the Data Science Interview is one book that isn’t affiliated with Gayle Laakmann McDowell’s original.

Cracking the Data Science Interview was written by Maverick Lin, a data scientist who compiled the book while completing data science interviews in 2019. Lin said his goal was to gather a cheatsheet of concepts he saw most frequently, which is the basis of this book: A high-level overview of core data science concepts, along with 100+ interview questions.

• There is a solid overview of the most frequently asked concepts. The 18 Big Ideas in Data Science section covers topics in-depth like Occam’s Razor, overfitting, bias/variance, along with sections on machine learning, case studies, and data wrangling.
• The cheatsheet-style text is perfect for quick reviews, and the follow-up questions will give you the flavor of the questions you might face in these categories.
• Although this book covers much ground, the author’s explanations can sometimes be challenging to follow.
• Some solutions to the 100+ questions are incomplete, and reviewers complain about mathematical and grammatical typos (typical for self-published books).

This book provides a solid review of data science concepts, and before an interview, it’s always helpful to brush up on the basics. The questions are also practical. However, you’ll find more profound collections of data science interview questions online or in other references, and these alternatives typically have more in-depth explanations and solutions.

Heard in Data Science Interviews 2018

Heard in Data Science Interviews boasts a wide selection of 650+ data science interview questions across all the major topics, like algorithms, statistics, computer science, and data modeling.

Written by Kal Mishra, a data scientist with more than ten years of industry experience, this guide is intended to cut out “fluff” portions often found in interviews, focusing mainly on “genuine AI questions.”

• This book covers almost every topic that can be sure to come up in your data science interview– making this a powerful study tool for content review.
• The flash-card style text is excellent for skimming and quick review! This text may be the book for you if you’re looking for an easy reference to brush up on your knowledge before an interview.
• Unfortunately, the synopsis proves too good to be true: early readers complain of distracting grammar errors and an appendix with numerous mistakes across the answer key.
• Some questions are said to be overly vague and frustrating, only compounded by unclear explanations in the answer key.

While this book may be helpful for new interviewees looking for a comprehensive guide, persistent complaints of the errors in Heard in Data Science Interviews’ answer key make us hesitant to recommend it wholeheartedly.

At almost $50 (one of the highest price points on our list!), there is sure to be a better option without the glaring flaws in this text.

120 Data Science Interview Questions 2014

Written by data scientists for data scientists, this collection of questions covers specific data science topics: programming, stats, probability, etc.

Unique across all the other books on our list, “120 Data Science Interview Questions” also lists a Communication section designed to tackle those infamous interview questions asking you to describe certain concepts in non-technical terms.

• Reviewers have described this text as the unofficial data science edition of the “Cracking the Coding” guide.
• Questions are given in a case-study-like format, forcing you to think and investigate thoroughly while developing a solution. Unlike the pure knowledge tests (“Explain XXX”) found in other guides, the prompts here will more closely mimic the interview questions.
• At $19, this is one of our list’s most affordable study tools.
• Since this guide’s publication in 2014, data science has advanced rapidly with industry standards. As such, some of the sections (like Programming or Product Metrics) may contain outdated questions/answers.
• Purchasing this guide only provides access to roughly 25 crowd-sourced answers out of 120 questions. Given the format of the questions, it may be challenging to research answers for the other prompts on your own.

Out of all of the guides reviewed in this article, 120 Data Science Interview Questions offer the most fleshed out, interview-Esque questions typically found in data science interviews. This guide may be perfect for those looking to practice talking through solutions! For data scientists trying to establish a firm content foundation, you may need to look elsewhere for a more comprehensive reference with a complete answer key.

Becoming a Data Head 2021

Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning isn’t an interview book per se. However, it will help you think more critically about data and learn to ask the right questions, a skill that’s super beneficial in data science interviews.

Written by award-winning data scientists Alex Gutman and Jordan Goldmeier, this book will help you learn to avoid common data interpretation mistakes and provide an idea of the “types of personalities you will meet in the workplace.”

Becoming a Data Head will help you level up your data science vocabulary and brush up on critical data thinking skills. The book provides a solid accounting of real-world data science applications. It will help you embrace a mindset to ask better questions about the situations you encounter on the job.

This book won’t offer benefits to content review or practice questions for last-minute studying before an important interview. This book requires solid data science and machine learning knowledge to grasp the content thoroughly.

If you’re looking for help preparing for an incoming interview, this book may not be your best leading resource. While it provides valuable insights into data science and its application in modern businesses, it doesn’t dwell on interview structures or style. However, the book is super insightful and exciting, and it’s an excellent resource for building your data sense, a skill you will want to display in any interview.

The Data Science Handbook 2015

From the same authors of 120 Data Science Interview Questions comes “ The Data Science Handbook ,” a collection of 25 interviews with well-established data scientists about their perspectives in the field.

Unlike the other selections in this article, “The Data Science Handbook” doesn’t cover interviewing techniques or topics but discusses the career trajectories of successful data scientists navigating the industry.

• This handbook is FREE (small donations accepted) in ebook format and $25 on Amazon, making this an affordable resource for learning more about data science careers.
• Reading more about the perspectives of other data scientists may help you brainstorm for those tough culture-fit and ‘Why data science?’ types of questions.
• This handbook won’t be beneficial for content review or practicing questions for last-minute studying before an important interview.
• This 2015 published guide may be a little outdated if you’re looking for fresh perspectives on the data science field.

If you’re looking for help preparing for an incoming interview, this book may not be your best primary resource. While it provides valuable insights into the careers of many famous data scientists, candidates would better spend their time with other guides that focus directly on interview structures and style.

For those looking for general information about data science or those that may be interested in how different data scientists had their breakthroughs, read away!

Be the Outlier: How to Ace Data Science Interviews 2020

Written by data scientist Shrilata Murthy, Be the Outlier takes a different approach than most interview prep books; this book will help you understand how to position yourself as an outlier to land the job. In addition to a helpful concept review, the book also includes powerful resume-writing tips and in-depth accounting of what to expect in various interview formats like take-homes, presentations, case studies, and more.

• There’s plenty of helpful information on standing out, including using the 100-Word Story to structure your resume and tips for answering questions.
• Be the Outlier also provides insights into the “interviewer’s thinking,” which can help to demystify interviews.
• Although there’s a good selection of sample questions, it’s somewhat limited. However, the questions the practice questions include helpful explanations.
• The focus is primarily on early-stage data scientists or those newly graduated. You might want to consider an alternative if you’re looking for more advanced data science concepts.

This text is a solid prep book on data science, and it gives you a sense of what you can expect. The book also lets you know why questions get asked and what interviewers are looking for in your response. The one knock on this book is that the question bank is limited; you’d need to supplement your learning with additional questions.

Data Science Interviews Exposed 2015

Written by a collective of data scientists, “Data Science Interviews Exposed” was one of the first data science interview-guide books available on the market. In addition to the standard technical interview topics in many similar texts, this book reviews job search procedures and traditional screening interview processes.

• Readers looking to transition from other fields gave overwhelmingly positive reviews for the introductory chapters that discuss the data science field and job qualifications.
• The technical problem sets are slightly more complex and fleshed out than typical guidebooks, offering good practice for more experienced data scientists.
• Errors abound– unfortunately, early reviewers complain of serious grammatical errors and awkward sentence structures. While the answer key is more comprehensive than other texts we’ve reviewed thus far, mistakes in some solutions are another negative factor noted.
• The five sections before the problem sets may seem like unnecessary ‘fluff’ pieces for applicants who want practice for technical interviews.

At a price point of $50, this book isn’t the most cost-effective or efficient way to review for your data science interview. Newer data scientists may appreciate insights into job searches and soft skills. However, a new data scientist can also find these insights this information in collated online blog posts and resources from current data scientists. From a technical standpoint, this guidebook may not be the best resource depending on your experience level and the number of questions.

Ace the Data Science Interview 2021

Since its release in 2021, Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street has quickly become a favorite. The resource, co-authored by ex-Facebook employees, features the most in-depth question set in our list, helpful interviewing tips, resume writing advice, and tips for crafting a portfolio.

The guide’s 201 questions feature detailed step-by-step solutions (some of the most comprehensive of these books). The material covered includes probability, statistics, machine learning, SQL, Python, product metrics, database design, and A/B testing.

• This book offers some of the easiest-to-follow solutions for open-ended case study questions and will show you how to combine your product sense, stats, and modeling skills in your responses.
• One unique point is that This book provides actionable ideas to help you get your foot in the door and land an interview.
• The machine learning review is a bit shallow, and if you’re interviewing for a machine learning-intensive role, you’d need to supplement with other ML-focused resources.
• Overall, the material is very junior-level, aimed at those interviewing for entry-level or internship data science roles.

Use this book as a benchmarking tool; you can use it to understand where your strengths and weaknesses lie before you jump into the interviewing process. The book is a solid premier, especially for early-career data scientists. There’s much helpful information about how to land interviews, build your resume, what to wear, and what you can expect in the interview room.

Are Interview Books Still Useful in Data Science?

After reviewing these prep guides, it’s evident that no book covers all the possible topics for a successful data science interview. However, interview books can be helpful for data scientists, but they’re just one tool.

Mock interviews , coaching , and practicing SQL , Python , statistics , and other real data science interview questions are all vitally important. Because the field constantly evolves, you won’t find the most current information in a textbook.

Therefore, as you prepare for data science interviews, diversify how you study. Use books to benchmark where you’re at, then find more tailored interview resources to practice and brush up on the skills that need work.

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About the authors

Nick Singh started his career as a Software Engineer on Facebook's Growth Team, and most recently, worked at SafeGraph, a geospatial analytics startup. He currently writes career advice on LinkedIn to his audience of 120,000+ followers, and runs the SQL Interview platform DataLemur.com . He holds a degree in Systems Engineering with a minor in Computer Science from the University of Virginia. In college, he interned at Microsoft, and on the Data Infrastructure team at Google's Nest Labs. His career advice has been read by over 10 million people on LinkedIn. For fun, he likes to DJ, dance, and stan Drake.

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. On the side, he’s helped hundreds of people land data

jobs at companies including Apple, Lyft, and Citadel.

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