This article was updated after publishing the results of our survey of 20+ prominent data scientists, in which we asked them, amongst other things, what books had helped them grow the most in their careers.
In the past weeks, we asked successful data scientists in different industries what books they believe are the best to learn and master the necessary skills to become a data scientist. The books selected on this list come from their suggestions.
Data science, business analytics, and machine learning have become critical to numerous aspects of business, while the data scientist profession keeps soaring as one of the most in-demand and well-paid in the tech industry.
The books reviewed in this article have all received positive feedback from their readers, and they cover a wide range of skills and aspects that a successful data scientist has to master in their career, including statistics, data mining, machine learning, Python, business analytics, and more.
Some of the books are particularly suitable for people who want to transition into a data science career by learning the fundamentals. Other books are intended for those already in the field who aim to upgrade their competence and advance their career.
The authors of the books include prominent data scientists, researchers, and scholars from major universities.
Pro tip: check out also our article on the best artificial intelligence books and the survey results with inspiring data scientists to follow where we asked them which books helped them grow in their careers.
Best Data Science Books
- Best Data Science Books: Quick Comparison Chart
- Best Data Science Books
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction – Editor’s choice
- Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking – Runner up
- Practical Statistics for Data Scientists: 50 Essential Concepts – Best to Understand Statistical Concepts
- The Book of Why: The New Science of Cause and Effect – Most Thought-Provoking
- Machine Learning: A Probabilistic Perspective – Best for Researchers and Experts
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow – Great for Acquire Practical Skills
- Deep learning with Python – Best to Grasp Deep Learning
- Pattern Recognition and Machine Learning – Best for Theoretical Machine Learning
- Learn Python 3 the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code – Best for Absolute Beginners
- Deep Learning (Adaptive Computation and Machine Learning series) – Best for Intermediate and Advanced Students
- Credits
The 10 Best Data Science Books as of 2023
| Picture | Title | Price* | Author | Year |
 |
The Elements of Statistical Learning: Data Mining, Inference, and Prediction | $42-$80 |
Hastie, Tibshirani, Friedman | 2016 |
 |
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking | $8-$33 |
Provost, Fawcett | 2018 |
 |
Practical Statistics for Data Scientists: 50 Essential Concepts | $20-$28 |
P. Bruce, A. Bruce | 2019 |
 |
The Book of Why: The New Science of Cause and Effect | $0-$19 |
Judea Pearl | 2019 |
 |
Machine Learning: A Probabilistic Perspective | $49-$104 |
Kevin P. Murphy | 2012 |
 |
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow | $37-$44 |
Aurélien Géron | 2019 |
 |
Deep learning with Python | $0-$40 |
Francois Chollet | 2015 |
 |
Pattern Recognition and Machine Learning | $41-$80 |
Christopher Bishop | 2011 |
 |
Learn Python 3 the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code | $17-$18 |
Zed Shaw | 2019 |
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Deep Learning (Adaptive Computation and Machine Learning series) | $32-$63 |
Courville, Goodfellow, Bengio | 2017 |
*Please notice that prices can fluctuate, we recommend checking on Amazon
for the most accurate pricing information.
Reviews of the best data science books
The Elements of Statistical Learning: Data Mining, Inference, and Prediction – Editor’s choice
This book was mentioned by three of the prominent data scientists that we surveyed for the impact it had on their careers.
The book is very comprehensive and has sufficient technical illustrations to understand machine learning. The book goes into depth about the most used learning techniques via mathematics. Readers are happy to have this as a reference book compared to others in the market. With attractive colors and without the usage of too many mathematical theories, the book never makes the reader feel bored. The book is suggested for graduate-level mathematicians. Not even the information, but the readers are also happy about the quality of the paper used and the quality of the print.
Robert Tibshirani (Author) is well-known for his contribution to the LASSO method and Analysis of Microarrays. Generalized Additive Models, An Introduction to the Bootstrap, and The Elements of Statistical Learning are some of the well-known co-authored books by him. The author also has a Google Scholar Profile. He has received the COPSS Presidents Award in 1996. He has authored around 250 scientific articles.
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking – Runner up
This book was mentioned by Dr Iain Brown, Head of Data Science at SAS, a leading company in business analytics software and services.
“People who bought this book are happy with the introduction given to Data Science. It is a very useful book for anyone who wants to get into Data Science. The authors have tried to simplify complex topics into simple explanations which can be easily comprehended by anyone.
Author Foster Provost is an award-winning researcher and has co-founded several successful companies in the field of data science and marketing and Tom Fawcett is a Ph.D. holder in the field of machine learning. He has more than 20 years of experience in the field of R&D. Some of the major companies he has worked with include GTE Laboratories, NYNEX/Verizon Labs, and HP Labs. He has published several books related to data science which is read by many.”
Practical Statistics for Data Scientists: 50 Essential Concepts – Best to Understand Statistical Concepts
This book was mentioned during our survey by Admond Lee, data scientist, speaker, and writer.
“This book has a Decent review of core concepts related to Data Science, Better discussion of bootstrapping, and Good ideas on dealing with non-normal data. Readers are happy with the explanation given in the book and are happy with the way information is presented. Though this book is not designed for someone who is looking for deep knowledge in this field, this can definitely help as a guide to reach a higher level and help to understand basic concepts clearly.
Author Peter Bruce(Founder of the Institute for Statistics Education at Statistics.com) offers about 100 courses in statistics through his website and Andrew Bruce has over 30 years of experience in statistics and data science in academia. Andrew Bruce has developed statistical solutions for problems faced by various industries and firms.
Overall the book has a very good quality of information and is loved by the readers.”
The Book of Why: The New Science of Cause and Effect – Most Thought-Provoking
This book was mentioned during our survey by Manu Carricano, Director Institute for Data-Driven Decisions, ESADE Business School.
Readers are happy with the way the author establishes a relationship between two causes which is easily understandable by anyone. The book also contains examples from the medical field and other disciplines. The book has a very good introduction to causal inference for the general public. References made to Artificial Intelligence are really interesting and keep the readers stuck to the book.
Judea Pearl, Author (Professor of Computer Science at UCLA) is known for his works like Artificial Intelligence, Causality, and Bayesian Networks. He has studied at New York University Tandon School of Engineering and has got several awards including the Turing Award, the highest distinction in computer science from the Association for Computing Machinery for his significant contribution to the field of Artificial Intelligence. Overall, the book is really interesting and it is highly suggested for people who are interested in learning about new developments in casualty.
Machine Learning: A Probabilistic Perspective – Best for Researchers and Experts
This book was mentioned during our survey by Ganna Pogrebna, Head of Behavioural Data Science, The Alan Turing Institute.
Readers are happy about the quality of information that is printed in the book. With better pictures and good explanations, this book serves as the best reference for anyone who has a basic knowledge of Machine Learning. Readers are also happy with the statistical information provided in the book which is necessary to understand Machine Learning. The book has got a good response from the British Computer Society, David Blei (Princeton University), and several others. The author Kevin Patrick is an experienced writer who has deep knowledge in the field has got his Ph.D. from UC Berkeley, a Postdoc at MIT, and has worked as a research scientist at Google. He has published more than 50 articles in several journals. Though this book cannot act as an all-in-one source for complete beginners, with good pictures and better statistical information, this book can surely help as a reference for people to learn Machine Learning.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow – Great to Acquire Practical Skills
People who bought the book are very happy with the information packed in the book. With more information compared to the previous edition, this book has helped a lot to readers. With a slight touch of humor, customers never feel bored at any point while reading. The book helps a lot in learning as well as doing. Customers feel that the author has good experience in the field of machine learning and thus has a good grip on the topic. With the usage of a variety of colors and easy-to-understand language for all readers, this book stands out compared to others in the market. The author also has a decent number of followers on Twitter(11.9k+) and had led the YouTube video classification team for 3 Years. With detailed instructions regarding the topic, customers are of the opinion that this book can help anyone whether he is a beginner or an expert in the field of machine learning.
Deep learning with Python – Best to Grasp Deep Learning
Readers are happy with the explanation given in the book and also the author adding his perspective to it has made it more interesting and easily understandable. This book can help anyone interested in Deep learning whether he is a student, software developer, or teacher, this can help them to learn from scratch and help them broaden their knowledge regarding Python. The author has tried to prevent mathematical notation and has explained quantitative concepts via code snippets.
The Author François Chollet works on deep learning at Google and is known for the creation of the Keras deep-learning library and contributor to the TensorFlow machine-learning framework. The author is always into deep-learning research and his works have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.
Pattern Recognition and Machine Learning – Best for Theoretical Machine Learning
Based on the reader’s opinion, it is clear that the book is well written. With incredible clarity and best diagrams, the book is well organized. The book has an excellent stage setting and thus helps readers to stay on track while reading without missing any important topics. Although for the first time the book may not give complete clarity, a person with basic knowledge of linear algebra, probability, calculus, and some statistics can get the contents of the book easily. Readers are also happy that it doesn’t contain tons of mathematical demonstrations which makes it unique from others in the market.
The author Christopher Michael Bishop is known for his work Pattern Recognition and Machine Learning (PRML), has been awarded Tam Dalyell prize in 2009 and the Rooke Medal from the Royal Academy of Engineering in 2011. He is also a Laboratory Director at Microsoft Research Cambridge and a professor of Computer Science at the University of Edinburgh.
Learn Python 3 the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code – Best for Absolute Beginners
Readers liked the simple way of explanation given in the books. Even the most complex concepts of the topic are explained in a simple and easily understandable way which keeps the readers glued to the books. The book is well structured and it is aimed at complete beginners. But even experienced programmers have expressed their good opinions regarding the book. The book gives a straight-up introduction to Python without useless stories which made readers happy. There are not many confusing codes and it has direct information which makes it stand out when compared to others.
Zed Shaw, the Author is a multitalented guy who has vast experience in the field of programming. The software designed by him is used in many large and small-scale companies. He has written many popular essays.
Deep Learning (Adaptive Computation and Machine Learning series) – Best for Intermediate and Advanced Students
Readers are happy with simple math illustrations without too many unnecessary details. The introduction is really good and has a good recap of the history of computers. It covers the latest areas of Deep Learning which keeps the users up-to-date with the topic. The book is best suited for someone who is interested in Deep Learning at a research level and not much suggested for beginners, but it can surely help as a good reference.
Authors of this book are Ian Goodfellow( Google Research Scientist with 175K+ Followers on Twitter), Yoshua Bengio(Computer Science Professor at the Université de Montréal), Aaron Courville(Assistant Professor of Computer Science at the Université de Montréal). All of them are popular on the internet and are well experienced and have great knowledge in the field of machine learning. With the combined effort of all these experts, this book really stands out of the crowd and is referred to as the Theoretical Bible of Deep Learning by readers.
Credits
For their special contribution to identifying the best reads in the field of data science, we would like to, particularly thank:
Kaushal Paneri, Data Scientist at Bing Ads
Andrea Mezzalira, Research Data Scientist at Amazon
Felipe Ducau, Data Scientist and Machine Learning Researcher at Sophos
