# 54 Best Data Science Books in 2022 (Vetted by Experts)

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.

### 1. *Weapons of Math Destruction* (September 2016)

**Rating**: 4.5/5 (3,017)

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.

### 2. *Big Data: A Revolution That Will Transform How We Live, Work, and Think* (March 2014)

**Rating**: 4.3/5 (827)

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.

### 3. *Naked Statistics: Stripping the Dread from Data* (January 2014)

**Rating**: 4.6/5 (2,236)

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.

### 4. *Invisible Women: Data Bias in a World Designed for Men* (March 2019)

**Rating:** 4.7/5 (8,353)

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.”

### 5. *Numsense: Data Science for the Layman* (March 2017)

**Rating**: 4.5/5 (299)

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).

### 6. *Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are* (May 2017)

**Rating**: 4.4/5 (2,295)

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.

### 7. *Algorithms of Oppression: How Search Engines Reinforce Racism* (February 2018)

**Rating**: 4.7/5 (500)

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.

### 8. How to Lead in Data Science (December 2021)

**Rating**: 4.9/5 (30)

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.

### 9. Ace the Data Science Interview (August 2021)

**Rating**: 4.5/5 (577)

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.

### 10. Data Science for Economics and Finance (June 2021)

**Rating**: 4.7/5 (39)

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.

**General Data Science Books**

### 1. *The Elements of Data Analytic Style* (March 2015)

**Rating**: 3.9/5 (12)

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.

### 2. *The Art of Data Science* (June 2016)

**Rating**: 4.6/5 (44)

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.

### 3. *An Introduction to Data Science* (September 2017)

**Rating**: 4.4/5 (74)

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.

### 4. *Social Media Mining* (April 2014)

**Rating**: 4.8/5 (14)

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.

### 5. *The Data Science Handbook* (March 2021)

**Rating**: 4.4/5 (53)

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.

### 6. *Doing Data Science: Straight Talk from the Frontline* (October 2013)

**Rating**: 4.3/5 (170)

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.

### 7. *Data Science for Dummies* (March 2017)

**Rating**: 4.4/5 (204)

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.

### 8. *Data Jujitsu: The Art of Turning Data into Product* (November 2012)

**Rating**: 4.1/5 (141)

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.

### 9. *Mining of Massive Datasets* (September 2014)

**Rating**: 4.4/5 (54)

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.

### 10. *Designing Data-Intensive Applications* (May 2017)

**Rating**: 4.8/5 (3,098)

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.

### 11. *Data Science Job: How to Become a Data Scientist *(January 2020)

**Rating**: 4.0/5 (26)

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.)

### 12. Becoming a Data Head (May 2021)

**Rating**: 4.6/5 (171)

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.

**Python for Data Science Books**

### 1. *Python Data Science Handbook* (January 2017)

**Rating**: 4.6/5 (586)

An O’Reilly text by Jake VanderPlas, this book is also available as a series of Jupyter Notebooks on Github. It’s not for total beginners since it assumes some knowledge of Python programming basics. (But don’t worry – we’ve got an interactive Python course you can take for that).

### 2. *Automate the Boring Stuff with Python* (November 2019)

**Rating**: 4.7/5 (2,494)

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.

### 3. *A Byte of Python* (September 2013)

**Rating**: 3.9/5 (9)

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.

### 4. *Learn Python, Break Python* (February 2014)

**Rating**: 4.0/5 (11)

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.

### 5. Data Science from Scratch (May 2019)

**Rating**: 4.4/5 (589)

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.

### 6. Python for Beginners (August 2021)

**Rating**: 4.1/5 (14)

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.

### 7. Data Science Projects with Python (July 2021)

**Rating**: 4.6/5 (49)

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.

**R for Data Science Books**

### 1. *R Programming for Data Science* (April 2016)

**Rating**: 4.2/5 (20)

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.

### 2. *An Introduction to Data Science* (September 2017)

**Rating**: 4.4/5 (74)

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.

### 3. *Advanced R* (May 2019)

**Rating**: 4.8/5 (143)

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.

### 4. *R Cookbook* (July 2019)

**Rating**: 4.6/5 (102)

Precisely what it sounds like: a collection of R “recipes” for data analysis and data science work.

### 5. *R Graphics Cookbook* (November 2018)

**Rating**: 4.5/5 (77)

Similar to the above, this is a cookbook that’s focused specifically on getting higher-quality graphs and charts out of R.

### 6. *R for Everyone* (June 2017)

**Rating**: 4.4/5 (235)

This is an actual R programming textbook, focused on teaching R from scratch. Unlike many other R textbooks, it approaches the subject without the assumption that the reader already has a deep knowledge of statistics.

**Machine Learning Books**

### 1. *Neural Networks and Deep Learning* (August 2018)

**Rating**:4.5/5 (164)

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.

### 2. *Bayesian Reasoning and Machine Learning* (March 2012)

**Rating:** 4.2/5 (83)

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!

### 3. *Understanding Machine Learning: From Theory to Algorithms* (May 2014)

Ranking: 4.4/5 (218)

Looking for a thorough review of machine learning, from the fundamentals, all the way through advanced machine learning theory? Look no further.

### 4. *Deep Learning* (November 2016)

**Rating**: 4.3/5 (1850)

This textbook from MIT Press is only available in HTML format. It covers everything – from the basics, through current research, into deep learning.

### 5. *Machine Learning Yearning* (November 2018)

Ng says that courses teaching technical skills can give you a “hammer.” This book aims to teach you how to use that hammer correctly.

### 6. *Natural Language Processing with Python* (August 2009)

**Rating**: 4.4/5 (179)

This is a great text for anyone interested in NLP. The online version has been updated with Python 3.

### 7. *Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow* (October 2019)

**Rating**: 4.8/5 (3,236)

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.

### 8. *Grokking Deep Learning* (January 2019)

**Rating**: 4.4/5 (134)

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!

### 9. *Deep Learning with Python* (December 2017)

**Rating**: 4.5/5 (1,340)

Another Python-focused deep learning and machine learning text. This one is focused primarily on using the Keras library.

### 10. Data Science on the Google Cloud Platform (May 2022)

**Rating**: 5/5 (10)

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.

**Statistics Books**

### 1. *Introduction to Probability* (January 2008)

**Rating**: 4.5/5 (203)

Don’t overthink it. This book is precisely what it sounds like: an introductory textbook that teaches probability and statistics.

### 2. *Think Bayes* (June 2021)

**Rating**: 4.5/5 (41)

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.

### 3. *Bayesian Methods for Hackers* (October 2015)

**Rating**: 4.3/5 (128)

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.

### 4. *Statistical Inference for Data Science* (May 2016)

A rigorous look at statistical inference. This one is for readers who are already somewhat comfortable with basic statistics topics and programming with R.

### 5. *An Introduction to Statistical Learning* (July 2021)

**Rating**: 4.7/5 (132)

A great introduction to data-science-relevant statistical concepts and R programming.

### 6. *The Elements of Statistical Learning* (April 2017)

**Rating**: 4.6/5 (1,002)

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.

### 7. *Data Mining and Machine Learning** *(January 2020)

**Rating**: 4.7/5 (18)

This Cambridge University Press text will take you *deep* into the statistics and algorithms used for various types of data analysis.

### 8. *Think Stats: Exploratory Data Analysis* (November 2014)

**Rating**: 4.2/5 (132)

Another stats text focused on statistics in the context of data analysis work using Python.

### 9. Practical Statistics for Data Scientists (June 2020)

**Rating**: 4.6/5 (637)

A book on statistics specifically for data scientists! This 2nd edition includes valuable Python examples.

**…But Don’t Just Read Books!**

Reading (or listening to) books can be a great way to augment your data science learning. But the best way to learn anything, including data science, is to get hands-on and actually do it. Write the code you’re reading about. Collect your own data. Build your own models. Learn by doing.

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.

Give it a try — what have you got to lose?

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