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

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!

A reader review:

“The book is well written and provides good insights into how to form a foundational core to further one’s education and experience in data analysis and visualization. To truly accomplish the results that significantly impact informed decisions, one must bring a well-rounded background and experience to the table. The book serves as an excellent source to focus on a positive approach to learning and executing data science. Not all will agree, but in my opinion, the secret to success in this area is never to evolve your skill isolated from the works of those already successful and willing to share their knowledge.”

Buy it on Amazon now.

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

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.

A reader review:

“Content is extremely well written; you’ll learn the fundamentals of data science and gain an understanding of how data can be used to model different situations, as well as the mathematical/practical methods to do so.”

Buy it on Amazon now.

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

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.

Reader reviews:

“An easy to read (and understand) introduction to the data analysis workflow. This should be required reading for learners and new practitioners alike. The only shortcoming of this book is the lack of a more thorough treatment, by way of a detailed example, of a fully worked data analysis project.”

“Excellent book for data science beginners. Covers the general data analysis process in a very clear way.”

Buy it on Amazon now.

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

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.

A reader review:

“This book is suitable for people with basic python programming skills. It is very good for beginners and advanced users alike. The codes are very clear and without errors. This book teaches you the basics and introduce some expert level topics for you to explore further if keen. If you are a novice data analyst and some harder topics throw you off, you should probably revisit the topics after you have gain more knowledge on data science. I highly recommend this book as your first book into data science because the codes and thought processes are very clear. 70-80% of the book are data science foundation and basics for you to tackle harder topics later.”

Buy it on Amazon now.

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

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.

A reader review:

“This book is exceptional and a must read for anyone in the data space. The authors not only provide information about best practices, but also historical and market context as how we got to this point. In addition, their inclusion of additional resources within each chapter is a gold mine of information to dive deeper. For my work personally, I’m scaling up our data team’s ability to enable easier access to data for analytics and build the foundation for ML use cases. This book is serving as a great point of reference on this journey. Finally, I have been leading a book club at my job where we are currently reading this book. We finally have common language among Data Sci, Eng, and other business stakeholders in the book club to discuss how we can improve our data infrastructure to better serve the business. You must get this book!”

Buy it on Amazon now.

6- Data Science For Dummies by Lillian Pierson

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.

A reader review:

“Data Science for Dummies is the most comprehensive data book I’ve ever read. Leaving no stone unturned, this book provides an in-depth tour of all facets of working in data, including big data, statistics, algorithms, business cases, and the different types of data workers. This book provides a thorough understanding of every area data touches. I learned many new things – and I highly recommend it to anyone who works with data (even if they’re not a data scientist) or to anyone simply interested in having a broader understanding of the entire field.”

Buy it on Amazon now.

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

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.

A reader review:

“In a world where data continues exponential growth trajectory one needs to be able to make sense of the data. Many times people take data visualizations and statistical reports at face value. If only there was a book to help people cut through the noise of Big Data, Machine Learning, and Artificial Intelligence. The book “Becoming A Data Head” is that book that helps readers cut through the noise and hype of these new realms of leveraging data! The book provides tools and details on how readers can better understand the merits of machine learning, artificial intelligence, and overall use of data. There are also chapters on probabilities, statistics, and regression equations that help set the foundation for the more advance methodologies. I love how the authors break down these complex ideas so that readers can be skeptical and challenge assumptions. In addition readers will know how to ask better questions at the start of a project, so that the data analytics project will provide meaningful results.”

Buy it on Amazon now.

Best Data Science Books for Advanced Data Scientists

8- Python Data Science Handbook by Jake VanderPlas

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.  

A reader review:

“This is an excellent reference book for people working with data science. Remember, 80% of the effort in machine learning, data analysis, or data science, in general, is about processing data and understanding data. This book is for that purpose, and I think it’s the best book out there about data processing, analysis, and visualization using python. If you look for hardcore machine learning, go for other books. Highly recommended!”

Buy it on Amazon now.

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

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.

A reader review:

“This book is a complete reference on state-of-art deep learning, unlike some reviewers indicated otherwise. Most of the subjects in the area were covered and explained well. I don’t know if any other expert could describe all these DL theories better. My favorite chapter is “Practical methodology,” which provides ways to architect your models from scratch to the end. I think the chapter itself is worth the price of the book. You will never master this subject from reading “deep learning” python programming books, and without mastering this, you will probably keep copying others’ code. If you can’t believe me, just search “deep learning” in google scholar; this book has already been cited by more than five thousand research papers.”

Buy it on Amazon now.

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

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. 

A reader review:

“It’s a great book to learn at your own pace the basics of data science and data manipulation with R, and I would recommend to anyone to buy it. The authors are precise and clear in the explanations. Just one comment: if you are a beginner with the language, you will have a harsh time going through the book. In the first 2 sections, where the authors explain ggplot and dplyr, if you don’t know the grammar of the code you will certainly get frustrated because there is no explanation of the basics. They just start with the ggplot code and take off. The same occurs with the dplyr chapter. They teach you how to use the functions filter(), arrang() and so on, but don’t show you the basics of R. My advice is to read other sources to get a basic understanding of R and its grammar, and then get this book.”

Buy it on Amazon now.

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: 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 a 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.

A reader review:

“I came to this with very little stats and linear algebra knowledge and no calculus. The author goes into just enough detail to be able to understand the math without getting overwhelmed, and the Python implementations really help break up the content and stick the math into your mind. The chapters build on each other with a final chapter on Neural Networks integrating everything you have learned previously. This chapter was a bit hard for me to completely follow and I plan to revisit it after some additional math training. This is a book I think I will go back to again and again through the next couple of years.”

Buy it on Amazon now.

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

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.

A reader review:

“What you will not find in this book is a comprehensive, thorough deep dive into each topic needed to be hired as a Data Scientist. It isn’t a text book to teach you those concepts. What this book does cover are the topics that could be asked during the interview process in a condensed format, as a review. This book assumes you have already done what it takes to learn the material thoroughly, through coursework and projects, and it gives you guidance on which topics are most important during the interview process, so you can sharpen those skills and have them available to be showcased during your next interview. Going through the book shows you where learning gaps exist, where concepts are a little rusty, and gives you an idea of the hierarchy of importance for the vast amount of skills required in this field. It is then up to you to reach for other resources to fill in those gaps. This book encouraged me to get uncomfortable, get out of my own way and make connections to land my first job as a Data Scientist. I highly recommend this book for anyone beginning their journey in data science. It gives you the roadmap on how to land that first role and has been a valuable resource not only for me, but for everyone I have recommended it to as well.”

Buy it on Amazon now.

Conclusion

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 forbooks 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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Mert Aktas

Mert is the Marketing Manager of UserGuiding, a code-free product walkthrough software that helps teams scale user onboarding and boost user engagement.