12 Data Science Books That Will Turn You Into a Data Scientist

Data is everywhere nowadays.

And you might want to learn data science from books to become a data scientist. Thus, you might require a starting point to start learning it from home.

Worry not ’cause the best way to start basic data science includes reading the resources that might count as gentle introductions and moving onto more advanced topics with time.

However, choosing the best book for data science from a wide range of resources can be a struggle, I know.

That’s why we have listed the best books for beginners who want to build a strong foundation for data science—we have recommendations for people who are already experienced in the science industry as well.

Below, you’ll find 12 data science books that you should read to become a data scientist, and some of these recommendations include the best data science books that you can find in PDF as well.

Now is the time to find the perfect complete guidebook for your needs and add it to your reading list!

Best Data Science Books for Beginners

1- Doing Data Science: Straight Talk from the Frontline by Cathy O’Neil and Rachel Schutt

Doing Data Science

Being the best book in data science, Doing Data Science: Straight Talk from the Frontline will show you how to take your data science skills further. The authors have been in the data science field for years.

Their expertise is evident throughout the book, which is full of valuable insight on everything from working with big data to building models to communicating with non-technical people.

Cathy O’Neil founded one of the first hedge funds to use algorithmic trading. After working as a quantitative analyst at various hedge funds, she became a math professor at Barnard College.

She also runs O’Neil Risk Consulting & Algorithmic Auditing LLC. Rachel Schutt has a Ph.D. in Statistics from Stanford University and worked as a Senior Statistician at Google during her time there, where she led many analysis projects. 

Why should you read this book?

It gives an insider view of what it’s like to be a data scientist. Anyone who wants to learn about data science but finds books in this area very technical or too difficult to understand can read this book with real-life examples. This book can be considered the best way to learn data science as a beginner, so go and grab a copy!


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2- Practical Statistics for Data Scientists by Peter Bruce, Andrew Bruce, and Peter Gedeck

Practical Statistics for Data Scientists

Practical Statistics for Data Scientists covers the essential concepts of statistics without getting into the mathematical theory behind it.

The book is written in an easy-to-read manner, with real-world examples, making it an excellent choice to educate people’s data science skills and machine learning. 

As well as helping readers understand the mathematics behind each concept, it also provides them with code written in base R to implement these concepts in their own projects.

The authors, Peter Bruce and Andrew Bruce have extensive experience working in data science.

They have each published several books on various topics related to this subject and are considered experts. The third author of this book, Peter Gedeck, is an experienced data scientist who has worked in this area for over thirty years.

Why should you read this book?

The book provides an introduction to statistical concepts of data science.

With this excellent resource, you can learn how to think critically about data and avoid common errors that could lead you down the wrong path. In addition, you can discover new ways to carry out exploratory data analysis and find hidden gems with this single book.


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3- The Art of Data Science by Roger Peng and Elizabeth Matsui

The Art of Data Science

The Art of Data Science is a valuable resource and an excellent introduction that helps you understand the basics of data science.

This book is a great step-by-step resource for beginners and helps to gain more knowledge of the terminology and theoretical concepts.

This book covers the basics of data science topics. Then, it gives real-life examples that help demonstrate how the data science concept can be used in everyday life. 

Roger Peng is a professor at Johns Hopkins and the author of R Programming for Data Science. Elizabeth is a professor at the University of California specializing in data science education.

Also, she has published dozens of research papers on subjects ranging from health policy to climate change impacts.

Why should you read this book?

The Art of Data Science is a great introduction for absolute beginners who want to learn about data science or improve their skills in this area.

It is a comprehensive guide with practical applications and one of the most popular books on this topic.


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4- Data Science from Scratch: First Principles with Python by Joel Grus

Data Science from Scratch

Data Science from Scratch is a book for beginners that manages to fulfill its promises, all guaranteed.

This surely includes paving the way for Python to hold up a significant place in your mind so that you can understand how algorithms work. Diving into the fundamental concepts, from linear regression to logistic regression, this practical guide helps you build a strong data science foundation to advance.

The author, Joel Grus, now leading a team that focuses on putting products regarding machine learning and data, started his career as a data scientist in several startups and once worked as a software engineer at the Allen Institute for AI and Google.

Why should you read this book?

First of all, the author has a great amount of knowledge on the fundamentals of machine learning, alongside with business perspective that could bring a new way of seeing.

Secondly, this book provides readers with a crash course in Python, making sure you pick up all the necessary elements regarding this popular programming language.

In short, anyone who wants to start studying data science should give this book a read.


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5- Fundamentals of Data Engineering: Plan and Build Robust Data Systems by Joe Reis and Matt Housley

Fundamentals of Data Engineering

As an inseparable part of data science, data engineering is so well-explained throughout Fundamentals of Data Engineering: Plan and Build Robust Data Systems that there is no way you wouldn’t benefit from the advantages of getting the bigger picture when it comes to the key concepts of data engineering right after you read this masterpiece.

Moreover, this book gives detailed explanations to make sure that you get the ropes on data engineering to make use of it in your own career to build a solid foundation, especially for data scientists.

Having 20 years of hands-on experience, Joe Reis has landed several science jobs, from data engineering to data architecture.

Now, he is the CEO and co-founder of his own data consulting firm. Matt Housley, on the other hand, is both a cloud specialist and data engineering consultant.

Co-founder of the aforementioned firm, there is no stop for them in their science careers for sure.

Why should you read this book?

‘Cause this practical book manages to take a deep dive into the world of data engineering to get you a new point of view (especially of data consumers) for you to plan and build systems while using real-world data.

Furthermore, you’ll become accustomed to basic concepts that will be of help in the data environment, such as data generation, data storage, data ingestion, and data transformation.


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6- Data Science For Dummies by Lillian Pierson

Data Science for Dummies

Data Science For Dummies is a great book for beginners who want to learn everything from scratch to be able to lead science projects on their own in the future.

The first two parts of the book include insights into data science as a career, business decision-making, and real-world applications, while the third part emphasizes more advanced topics, such as data science strategy and data monetization.

The author Lillian Pierson has 16 years of experience when it comes to producing technology products and delivering consulting services on strategies.

Right now, she is working as the CEO of Data-Mania to support data professionals who have real-world problems.

Why should you read this book?

Data Science for Dummies is totally worth both the read and hype since it’s one of a few books that can capture the essence of data science and provide readers with applicable methods that will help you plan out a roadmap regardless of the role you play in data.


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7- Becoming a Data Head: How to Think, Speak and Understand Data Science, Statistics and Machine Learning by Alex J. Gutman and Jordan Goldmeier

Amazon Becoming a Data Head

Becoming a Data Head: How to Think, Speak and Understand Data Science, Statistics and Machine Learning is a book that might count as an introduction to machine learning while touching upon a wide range of topics, from mathematical concepts such as statistical analysis to business intelligence.

It excels at conveying these topics as well as the tips concerning data business skills it provides readers with.

The authors, Alex J. Gutman and Jordan Goldmeier, who are recognized as experts in the field, are data scientists who actively work in the field while giving speeches based on their programming experience to teach data science to the next generations.

Why should you read this book?

One of the reasons why you should purchase this book definitely lies within its core: data head.

The authors do everything in their power to supply you with practical advice that might help you overcome a problem that you face during business hours as a data scientist— helping you have a data-driven mind is all it takes, after all.


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Best Data Science Books for Advanced Data Scientists

8- Python Data Science Handbook by Jake VanderPlas

Python Data Science Handbook

The Python Data Science Handbook is a collection of helpful tutorials and information on using data science tools like Python to analyze data.

This comprehensive book covers everything from how to use Python to manage data to use it for machine learning.

Also, you can learn to create your own machine learning algorithms without having any previous experience with machine learning with this in-depth guide.

Jake VanderPlas is a research scientist at the University of Washington, where he has worked since 2013. He has a Ph.D. in Astronomy from the University of Washington.

In addition to his work as an author and researcher, he gives workshops on data science, machine learning, and scientific software development worldwide.

Why should you read this book?

If you are curious about machine learning or artificial intelligence, this book is a great place to start your journey.

It goes step-by-step through each process that you’ll need to know to perform basic machine learning tasks such as classification, clustering, and regression using linear models.  


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9- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Deep Learning

Deep Learning is an exceptional book for those who want to learn about the fundamentals of deep learning.

This book provides a detailed introduction to modern deep learning for data science learners. In addition, it covers many developments in the field, including an overview of artificial neural networks and their importance in AI. 

Apart from these, the book covers many algorithms and practical methods for building deep networks. You can benefit from this technical book on your exploratory data analysis as well.

Ian Goodfellow, Yoshua Bengio, and Aaron Courville are all well-known figures in the area of artificial intelligence. They have authored more than 150 papers and are among the most cited researchers in their respective fields.

Ian Goodfellow is currently working at Google Brain. Yoshua Bengio is working at the University of Montreal as a professor and researcher. Aaron Courville is working at Mila as an associate professor.

Why should you read this book?

This book will be useful to students and researchers who want to get a comprehensive overview of the field.

It will also be useful to engineers who want to start using deep learning in their products. Along with books on statistics, deep learning books can be helpful in the data science area.


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10- R for Data Science by Hadley Wickham and Garret Grolemund

R for Data Science

R for Data Science is a book that teaches data science using the R programming language. In this book, the authors guide you through the steps of importing, exploring, and modeling your data and communicating the final results with practical examples. 

Hadley Wickham is Chief Scientist at RStudio, an active contributor to open-source software, and an Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University.

Garret Grolemund is the author of Hands-On Programming with R and co-author of R for Data Science and An Introduction to Statistical Learning with Applications in R (Springer). He teaches data science at Rice University on-campus and online MS program in statistics.

Why should you read this book?

By reading this book, you can understand the data science journey, along with statistical models and the basic tools you need to manage the details.

In addition, each section of the book is paired with exercises to help you practice what you’ve learned along the way. 


R for Data Science

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11- Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics by Thomas Nield

Essential Math for Data Science

Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics is on this side of the list due to the mathematical concepts it includes.

For example, an introduction to probability, insights into modern statistics, and calculus are some of the main concepts this book touches upon.

Moreover, this book doesn’t forget to shed light on Python so that you can get better at it to make more use of it throughout your career.

Thomas Nield, the founder of Nield Consulting Group, is also a business consultant that is proficient in Java, Kotlin, Python, SQL, and many more. Also, he often has classes on topics like AI system safety, mathematical optimization, and machine learning at the University of Southern California.

Why should you read this book?

This book is a must-read for people who want to have a strong foundation on the mathematical side to obtain a deeper knowledge of data science. For example, probability, statistics, and linear algebra.

Also, let me note down that the book splits into two main sections, one about math concepts while the other provides readers with practical insight regarding machine learning.


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12- Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street by Nick Singh and Kevin Huo

Ace the data science interview

The reason I put this book under the advanced category is that you need to know a lot about data science before applying for your dream job at the brands I’m about to name: Facebook, Google, Amazon, and Netflix.

Thus, it’s best to be prepared before reading this book to nail your next interview!

The authors, Nick Singh and Kevin Huo, were working for Facebook as a data scientist and growth team leader, respectively.

After Facebook, Huo became a data scientist at Hedge Fund, while Singh chose to run a SQL interview platform. Best to mention that Huo was once an intern at Facebook, Bloomberg, and on Wall Street as Singh interned at Microsoft and Google.

Why should you read this book?

To excel at answering the hardest questions that are thrown your way in interviews. Is that all, though? 

Of course, not. This book does surely include some solutions for 201 questions that are frequently asked during interviews, but it also includes tips regarding your dream position, such as crafting a resume, preparing a portfolio, and storytelling.

So, you might treat this book as a career guide as well.


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If you want to start basic data science but don’t know where to begin OR find data science hard to study alone and unsupervised, this article is the one for you as it provides you with all the required material that you can use for your benefit in order to start data science with no experience.

Moreover, you know you can learn data science at home; many did during the pandemic. Thus, don’t worry about where to start ’cause the introductory books on our list show you what you should learn first in data science along with a roadmap that can help you advance gradually.

Frequently Asked Questions

Which book is best for learning data science?

Suppose you are looking for a resource for beginners in data science. In that case, you can add the book The Art of Data Science by Roger Peng & Elizabeth Matsui to your reading list. On the other hand, if you are looking for books on statistics on this topic, consider reading Practical Statistics for Data Scientists by Peter Bruce, Andrew Bruce, and Peter Gedeck, which is a comprehensive guide.

Can I learn data science on my own?

Yes, you can! Data science learners can benefit from this list of books, including concepts of statistics. Also, by reading network analysis and programming books, you can improve your data science skills.

Where can I learn Python for data science?

You can learn Python for data science by reading books on this matter. For example, Python Data Science Handbook by Jake VanderPlas is a useful source that you can read to learn more about this issue. Also, there are many online sources to learn Python.

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