If you're serious about landing a Data Science job in 2023, 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.
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.
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.
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.
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:
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.
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.
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!
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.
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).
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 you need to read to ace your next Data Science or Machine Learning Interview are:
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.
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.