Top 10 Data Science Books to Read To Improve Your Knowledge

Data Science is becoming one of the hot topics for this generation due to the rapid digitization and resultant data flow. Considering the rising demand for data science professionals in the industry, most people are now looking forward to pursuing a full-fledged career in data science. 

Everything we use in our routine produces loads of data beyond our imagination. A study estimated that we produce approximately 2.5 quintillion bytes of data in a single day and counting! Moreover, the market size of data sciences was estimated at US$96.3 billion in 2021 and is expected to gain US$378.7 billion by 2030.

Following the prominent stance of data science in the current industry, you need to stay abreast of all the changes and updates in the relevant field, and what could work better than a bunch of best data science books. Let’s talk about the top 10 data science books to read in 2023 that will provide you with all the necessary information to understand and keep up with the world of data science. 

Top 10 Data Science Books to Read in 2023

1. The Elements of Statistical Learning by Hastie, Tibshirani, Friedman

The Elements of Statistical Learning is also known as the Bible of Machine Learning, which rightfully claims its place in our list of best data science books. This book is written by various connoisseurs who give you in-depth knowledge of artificial neural networks, support vector machines, decision trees, and many other crucial data science elements.

The content of this book covers supervised learning, linear methods for regression and classification, additive models, trees and related methods, neutral methods, random forests, and much other amazing content. This book uses straightforward examples, illustrations, high-quality figures, and many other elements. It is one of the greatest books for those who want to learn about machine learning in the business world. 

2. Data Science for Business by Provost and Fawcett

This book introduces readers to the basic principles of data science, which is among the best data science books for beginners to gain familiarity with the topic. It walks you through “data-analytical thinking” comprehensively, which is necessary for beginners. You’ll also learn about the use of data sciences in the Business world through real-world examples from which all the readers can relate.  

The content of this book includes– understanding how data science fits in your organization, use of data science for competitive advantage, approaching business problems with data analysis, data mining process, applying data science principles while interviewing the candidates and much other related content. If you want a deep and thorough understanding of the intertwined relationship between data science and business, give this book a read!

3. Practical Statistics for Data Scientists by P. Bruce, A. Bruce

This book brilliantly explains the connection between statistics and data science that you may not find everywhere. Most courses or books you read show one aspect that overshadows the other. But with the help of this book, readers can understand the statistical method and its applications in data science. 

The content of this book includes an exploration of data analysis, how random sampling can reduce bias and yield higher-quality data assets, how to use regression, statistical machine learning methods, and many more. If you have a basic idea of R programming language and statistics, you can give this perfect combination of stats and data a read. 

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4. The Book of Why by Judea Pearl

The book consists of diverse examples from the medical field and other disciplines, curated for the general public to get started with the concept of data sciences. The book covers the statistical and philosophical perspectives of data.

The contents of this book comprise an unparalleled combination of casual interference explored through the philosophical and statistical stance while following themes such as counterfactuals, big data, and artificial intelligence. It combines technology and philosophy perfectly, making it unique from the entire lot. 

5. Machine Learning by Kevin P. Murphy

Kevin P. Murphy makes his readers understand machine learning concepts that play a major role in the technological world. This book works as a comprehensive approach from the writer that makes its reader understand diverse probabilistic machine learning models.

It covers standard machine learning topics in-depth, such as probability, optimization, and linear algebra, forming the book’s foundation. The book reads informal, comprehensive, and accessible style, which is a great point to consider for a beginner. All the contexts covere

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d in this book are illustrated with the help of images, figures, and real-world examples. This book goes well for upper-level undergraduates with introductory knowledge of college-level mathematics. 

6. Deep Learning with Python by Francois Chollet

This book introduces readers to the field of deep learning from Python’s perspective. It builds a strong foundation with some of its unique concepts, intuitive explanations, and practical examples. 

This book’s contents include mathematical blocks of neural networks, fundamentals of machine learning, deep learning for computer vision, deep generative learning, and many more. The reader can also expect some challenging concepts and practices. After finishing this book, you’ll have a basic understanding and some hands-on skills to apply the concept of deep learning to your projects. 

7. Pattern Recognition and Machine Learning by Christopher Bishop

The book explains some of the most complex applications of machine learning by presenting comprehensive examples. It tells us about the practical applications and understanding of underlying algorithms and techniques. This book starts with an extensive introduction to dive deep into its analytical features and a detailed machine learning analysis.

The contents of this book include– probability theory, decision theory, the Gaussian distribution, Neutral networks, Sequential data, and many other vast topics. This is a reader-friendly book, even if you don’t have any prerequisite knowledge of pattern recognition or machine learning. 

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8. Deep Learning by Courville, Goodfellow, and Bengio

Deep learning is a significant part of machine learning that plays a crucial role in enabling computers to function like human brains. This book presents a wide range of topics in deep learning, mathematics, and some of the conceptual background of technology. 

The book’s content covers some of the relevant concepts of linear algebra, probability theory, numerical computation, and machine learning– to name a few. It presents the hierarchy of concepts by initially building the fundamentals. It also covers machine learning insights that every reader should be familiar with. If you’re planning to make your career in machine learning, this book is a good start. 

9. Storytelling with Data by Cole Nussbaumer Knaflic

This book educates you about the underlying data visualizations and how data can be used for visual storytelling, making it the best data science books for beginners. It teaches you to incorporate data in your conversations and also how important it has become in our daily lives.

This book defines the art of storytelling by going beyond conventional tools. The contents of this book are– choosing compelling visuals, focusing on the audience’s attention, thinking like a designer, lessons in storytelling, case studies, and many other things along the same lines. This book is fascinating since it teaches you to use data in your storytelling. 

10. Head First Statistics by Dawn Griffiths

This book profoundly explores the significance of statistics and clears all the fundamental doubts one can have in a very consumable manner. Whether a student or a working professional, this book presents insights into everything stats using engaging content, stories, quizzes, practical examples, and many other attractive elements that stick with the reader until the end. 

Looking at the contents of this book, we have– probability, normal, binomial, geometrical, and Poisson distributions, chi-square analysis, and many more. Give it a read if you want to understand the world of statistics easily.

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To go beyond the spectrum of books, get yourself enrolled with the Master of Science in Data Science offered by IIIT-Bangalore and LJMU. This course provides you with both fundamental and complex themes of data science. The key highlights of this course are– three unique specializations, six months of master’s project/ thesis, career coaching and mentoring session, networking opportunities, and many more under one course. 


Data science has become a crucial part of today’s world and is being leveraged by almost every industry. Hence, owning proficiency in the field can open countless career opportunities if we stay abreast of the ongoing data science trends. In order to keep yourself relevant in the dynamic world of data science, check out these books and certifications to boost your candidature!

Q. Is data science in demand in 2023?

Ans. Data science is one of the most in-demand fields in today’s era because of its rapid data expansion and its value among businesses. Moreover, the market size of data science is about to expand to US$378.7 billion by 2030, which further cements its future growth prospects.

Q. What book should I start with for data science?

Ans. Apart from the books mentioned above, Data Sciences for Dummies by Lillian Pierson covers all the basics you need before getting deep into it.

Q. Which book is the best to learn data science using Python?

Ans. Python for Data Analysis is one of the best books that present both fundamental and advanced concepts of data science, explaining how data manipulation and processing can be done using Python.

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