The Best Data Science Interview Books

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

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.

The Pros:

• 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.

The Cons:

• 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).

Overall:

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 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.”

The Pros:

• 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.

The Cons:

• 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.

Overall:

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.

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.

The Pros:

• 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.

The Cons:

• 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.

Overall:

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: 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.”

The Pros:

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.

The Cons:

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.

Overall:

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.

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.

The Pros:

• 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.

The Cons:

• 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.

Overall:

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!

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.

The Pros:

• 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.

The Cons:

• 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.

Overall:

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.

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.

The Pros:

• 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.

The Cons:

• 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.

Overall:

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.

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.

The Pros:

• 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 Cons:

• 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.

Overall:

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.

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.