54 Best Data Science Books in 2023 (Vetted by Experts)
What are the best books for learning data science?
First things first: If you want to learn data science, the most important thing you can do is get your hands on some real-world data and start coding. Our learning platform is designed to help you do just that. Even if you’re not using Dataquest, your primary approach to learning data skills should be hands-on, not passive.
But what can you do to keep learning in those moments when you’re not sitting in front of a computer? Read some data science books!
As a student we recently spoke with pointed out, ebooks are a great way to immerse yourself in data science when you can’t actually get hands-on with code. Think of reading during a bus ride, for example, or while waiting in line at the grocery store.
You can also listen to books like you would podcasts. Just use an ebook app with a “read aloud” feature or opt to pay for an audiobook.
There are so many different data science books available, though. Which ones are worth the time? We’ve listed some of the best below. The good news? Many of these books are totally free!
Note: Some of the links below are PDF links. We’ve tried to link to the free versions of books where possible.
Non-Technical Data Science Books
These are books that can help motivate you to start or continue your data science journey. Or they may help you better understand important issues in the data science field. You won’t learn many practical skills from them, but they’re good reads that help show how data and statistics are used in the real world.
One of the most popular nonfiction works about how “big data” and machine learning are not as unbiased as they might appear. Written by a former Wall Street quantitative analyst.
A good “big picture” read about how data and machine learning are changing lives in the real world — and what else is likely to change in the future. If you’ve heard about the hype, but aren’t really sure how data science can affect things, this is a good place to start.
A good read on statistics and data for the layperson. If you’re interested in learning data science, but it’s been a while since your first math course, this is the book for you. Ideally, it will help you build confidence and intuition about how statistics are useful in the real world.
Understanding how biases in data can create inequalities in the real world is critical for anyone working with data. This book details how aspects of gender inequality can be traced to data that treats men as the “default.”
A self-described “gentle” introduction to data science and algorithms, with minimal math. This is used as a textbook in some university courses, and it’s a good place to start if you’re interested in data, but a little bit afraid of the math. (By the way, you don’t have to be good at math to learn coding. In fact, it doesn’t even really help).
This book is essentially Freakonomics for data science. It’s an interesting read, and it will help you learn how to answer different kinds of questions using data.
Another book about how algorithms contribute to inequality; this one focuses on search engines. Understanding algorithmic bias, the ways it’s created, and how it can be avoided is really important for anyone who wants to work with data.
If you’re interested in the soft skills necessary to become a leader in the data science field, this is a great handbook. You’ll learn crucial industry concepts like managing complex data projects, overcoming setbacks, and facilitating diversity amongst teams.
Another great career resource, this book cracks the code of data science interviews. It includes not only hundreds of actual interview questions from data science giants, but also tips for resume and portfolio building.
Looking for examples of data science success stories? This is the book for you. You’ll get a crash course in data science technologies. Then, you’ll take a deep dive into more than a dozen detailed examples of how data science has changed the fields of economics and finance.
This book by Johns Hopkins professor Jeff Leek is a useful guide for anyone involved with data analysis. It covers a lot of the little details you might miss in statistics lessons and textbooks. Since it's a pay-what-you-want book, you can technically get this one for free. Of course, we recommend making a contribution if you can.
This is another pay-what-you-want book. It takes a big-picture view of how to do data science rather than focusing on the technical nitty-gritty of statistical or programming techniques.
This introductory textbook was written by Syracuse professor Jeffrey Stanton. Not surprisingly, it covers a lot of the fundamentals of data science and statistics. It also covers some R programming. Still, some sections are worthwhile reading even for those who are learning Python.
This textbook from Cambridge University Press won’t be relevant for every data science project. But if you do have to scrape data from social media platforms, this is a well-rated guidebook. Note that the site also includes links to free slide presentations on related topics.
This book is a collection of interviews with prominent data scientists. It doesn’t offer technical or mathematical insight, but it’s a great read. It’s especially relevant for anyone thinking about data science as a career.
This book consists of a collection of talks from data scientists working at a variety of companies. It’s meant to cut through the hype and help you understand how data science works in the real world.
Laugh if you want, but these books provide good, clear introductions to a lot of important concepts. There’s also a Big Data for Dummies that’s worth taking a look at.
Catchy title aside, this book is a good read about general data science processes and the data science problem-solving approach. Plus, it’s written by DJ Patil, arguably the most famous data scientist in the United States.
A free textbook on data mining with, as you’d expect from the title, a specific focus on working with huge datasets. Be aware, though, that it’s focused on the math and big-picture theory. Thus, it’s not really a programming tutorial.
This book is more about data engineering than data science. Still, it’s a good read for any aspiring data scientist tasked with creating production-ready models or data engineering work. Note: This is not uncommon in data science roles, particularly at smaller companies.
A book on the non-technical side of learning data science — how to build your data science career. The world of data science changes quickly, but this book was self-published in 2020, so it’s relatively up-to-date. Plus, several reviewers say it’s a good read for beginners. (Dataquest also has a data science job application and career guide if you’re interested in something that’s both shorter and free.)
This book is not just for data scientists, which only adds to its appeal. It’s perfect for newcomers to the field. Why? It discusses data in laymen’s terms while also introducing readers to the lingo and culture of the industry.
This total beginner’s Python book isn’t focused on data science specifically. Still, the introductory concepts it teaches are all relevant in data science. Plus, some of the specific skills later in the book (like web scraping and working with Excel files and CSVs) will also be of use to data scientists.
Like Automate the Boring Stuff, this is a well-liked Python-from-scratch ebook. It also teaches the basics of the language to total beginners. It’s not data-science-specific, but most of the concepts it covers are relevant to data scientists. It has also been translated into a wide variety of languages, so it’s easily accessible to learners all over the globe.
Yet another well-liked Python-for-beginners tome! This one encourages readers to learn Python by “breaking” it and watching how it handles errors and mistakes.
This book approaches the task of teaching data science in Python by walking you through how to implement algorithms from scratch. It covers a variety of areas, including deep learning, statistics, NLP, and much more.
This book aspires to do the impossible – teach you everything you need to know about computer programming from scratch – all in one book. While it may not reach that goal entirely, it will certainly teach you a ton along the way. Note that this guide features Python 3 instruction.
A unique find, this book re-creates the experience of working in the field of data science. Readers who immerse themselves in this project-based workbook will come away with newfound skills – not just in Python, but in machine learning, data visualization, logistic regression, and more.
Roger D. Peng’s text will teach you the basics of R programming from scratch. This is a pay-what-you-want text. Note that for $20 you can get it with all of the mentioned datasets and code files.
This introductory text was already listed above, but we’re listing it again in the R section because it does cover quite a bit of R programming for data science.
This is precisely what it sounds like: a free online text that covers advanced R topics. It’s written by Hadley Wickham, one of the most influential voices in the R community.
This free online book aims to teach machine learning principles. It’s not the place to go to learn the technical intricacies of any particular library, and it’s written with the now-outdated Python 2.7 rather than Python 3. Still, there’s a lot of valuable wisdom here.
This is a massive 680-page PDF that covers many important machine learning topics. It was written for students who lack a formal background in computer science or advanced mathematics. Total newbies welcome!
This is a Python-focused machine learning textbook. It uses the scikit-learn and Tensorflow frameworks to explore modeling and build different types of neural nets.
Grokking means “understanding,” and that’s exactly what this book is focused on. Its goal is to help you understand deep learning well enough to build neural networks from scratch!
One of the newest books on our list, this one is a must-read if you want to learn how to build a data pipeline on GCP. We’re particularly drawn to its project-based approach utilizing a real-world business decision.
An O’Reilly text by Allen Downey that offers an introduction to Bayesian statistics. Note that there is updated Python 3 code for this book available here.
Here’s another free read on Bayesian statistics and programming. The cool thing about this one is that the chapters are in Jupyter Notebook form, so it’s easy to run, edit, and tinker with all of the code you come across.
A rigorous look at statistical inference. This one is for readers who are already somewhat comfortable with basic statistics topics and programming with R.
Another valuable statistics text that covers just about everything you might want to know, and then some. (It’s over 750 pages long!) Make sure you get the most updated version of the book here.
Dataquest’s online classes teach you everything that you need to become a data scientist in a hands-on, project-based format. From the moment you sign up (it’s free), you’ll be writing real code and working with real datasets.
