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Best Data Science Books

We scoured the web for every book on data science, compiled a list and ranked them by how often they were featured. Each of the books on this list was featured in at least two of the articles.

Recommendations from 148 articles, Barack Obama, Bill Gates, Elon Musk and 50 others.
Best Data Science Books
100 books on the list
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow book cover
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Concepts, Tools, and Techniques to Build Intelligent Systems
Aurélien Géron - 2017-04-09
Goodreads Rating
This practical book is perfect for programmers interested in delving into the exciting field of machine learning. With concrete examples, minimal theory, and Python frameworks like Scikit-Learn and TensorFlow, the author shows readers how to build intelligent systems capable of learning from data. From simple linear regression to deep neural networks, you'll gain an intuitive understanding of machine learning techniques and architectures while getting hands-on experience through exercises in each chapter. Dive into the machine learning landscape and start building intelligent systems today.
Deep Learning book cover
Deep Learning
Ian Goodfellow - 2016-11-01
Goodreads Rating
This book explores the fascinating world of deep learning, which teaches computers to understand the world through a hierarchy of concepts. It covers mathematical and conceptual background, techniques used in industry, and research perspectives. Readers will learn about relevant topics in linear algebra, probability theory, and more, as well as practical applications in areas like natural language processing and computer vision. Perfect for students or engineers looking to incorporate deep learning into their work. Supplementary material available on the website.
Data Science from Scratch book cover
Data Science from Scratch
First Principles with Python
Joel Grus - 2015-04-14
Goodreads Rating
This updated book on data science teaches you the tools, algorithms, and principles underlying data science. With a focus on Python 3.6, author Joel Grus shows you how to implement these tools from scratch. Whether you have a background in programming or mathematics, this book will help you master the fundamentals of data science such as statistics, probability, and machine learning. The book also covers newer topics like deep learning and natural language processing. With practical examples and exercises, you'll be able to clean, manipulate, and analyze data, and implement models like neural networks and decision trees.
Recommended by
Thorsten Heller
Big Data book cover
Big Data
A Revolution That Will Transform How We Live, Work, and Think
Viktor Mayer-schönberger - 2013-03-05
Goodreads Rating
Explore the revolutionary science of big data, which allows us to analyze vast amounts of information and draw surprising conclusions. Discover how this emerging technology will dramatically impact the economy, science, and society as a whole. From predicting the spread of diseases to identifying dangerous manholes, big data has the power to transform the way we think about business, health, politics, education, and innovation. However, with this power comes fresh threats, from the end of privacy to the possibility of being penalized for things we haven't even done yet. Discover what you can do to protect yourself from these hazards in this brilliantly clear and surprising work by leading experts in the field.
Introduction to Machine Learning with Python book cover
Introduction to Machine Learning with Python
Andreas Muller - 2016-11-15 (first published in 2015)
Goodreads Rating
Learn practical ways to build your own machine learning solutions using Python with this book! Perfect for beginners, authors Andreas Müller and Sarah Guido will guide you through the process of creating successful machine-learning applications with the scikit-learn library. You'll discover how to use machine learning algorithms and focus on the practical aspects of the process. Don't wait to unlock the full potential of machine learning with Python!
Data Science for Business book cover
Data Science for Business
What You Need to Know about Data Mining and Data-Analytic Thinking
Foster Provost - 2013-09-17
Goodreads Rating
Explore the fundamental principles of data science with Data Science for Business. This book is written by experts Foster Provost and Tom Fawcett, and teaches readers the "data-analytic thinking" needed to extract valuable business insights from collected data. With the use of real-world examples, you'll learn how to improve communication between stakeholders and data scientists, as well as how to participate intelligently in data science projects. Discover how data science can support business decision-making and gain a competitive advantage.
Recommended by
Kirk Borne
Naked Statistics book cover
Naked Statistics
Stripping the Dread from the Data
Charles Wheelan - 2014-01-13 (first published in 2012)
Goodreads Rating
Discover the exciting world of statistics and its real-world applications in Naked Statistics. In this engaging book, Charles Wheelan simplifies complex concepts and shows us how data and statistics can be used to answer pressing questions, from cheating schools to rising autism rates. With insightful examples, including Schlitz Beer marketers and Let's Make a Deal, Wheelan brings statistics to life and strips away the technical details to focus on intuition. For those who struggled through Stats 101 or anyone curious about the power of data, Naked Statistics is a must-read.
Python for Data Analysis book cover
Python for Data Analysis
Data Wrangling with Pandas, NumPy, and IPython
Wes McKinney - 2012-10-01 (first published in 2011)
Goodreads Rating
An essential guide for anyone looking to manipulate, clean, and analyze structured data using Python. This second edition introduces practical case studies and covers important libraries like NumPy and pandas, offering helpful tips for beginners and experienced programmers alike. Written by the creator of the pandas library, this book is a must-read for anyone involved in data-intensive applications with Python.
Practical Statistics for Data Scientists book cover
Practical Statistics for Data Scientists
50+ Essential Concepts Using R and Python
Peter Bruce - 2020-06-16
Goodreads Rating
This book is a must-have for aspiring data scientists who lack formal statistical training. It explores statistical methods from a data science perspective and provides practical guidance on their application. The second edition includes comprehensive examples in Python and advice on avoiding their misuse. You'll learn all about exploratory data analysis, random sampling, experimental design, regression, and more. Plus, the book covers unsupervised learning methods for extracting meaning from unlabeled data. If you're familiar with R or Python and have some exposure to statistics, this accessible reference will bridge the gap for you.
The Hundred-Page Machine Learning Book book cover
The Hundred-Page Machine Learning Book
Andriy Burkov - 2019-01-13
Goodreads Rating
This machine learning book covers everything modern machine learning has to offer and can be read in just one week. It's concise and up to date, written by an experienced practitioner. Plus, it has a continuously updated wiki with additional resources. With flexible pricing and formats, you can choose what suits you best, and you can even read the book chapters for free before deciding whether to buy.
Recommended by
Kirk Borne
Deep Learning with Python by François Chollet
Pattern Recognition and Machine Learning by Christopher M. Bishop
Storytelling with Data by Cole Nussbaumer Knaflic
The Elements of Statistical Learning by Trevor Hastie
An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
R for Data Science by Hadley Wickham
Python Data Science Handbook by Jake Vanderplas
Artificial Intelligence by Stuart Russell
Doing Data Science by Cathy O'Neil
Machine Learning by Kevin P. Murphy
The Signal and the Noise by Nate Silver
Machine Learning For Absolute Beginners by Oliver Theobald
Data Science For Dummies by Lillian Pierson
Programming Collective Intelligence by Segaran
Too Big to Ignore by Phil Simon
Big Data at Work by Thomas H. Davenport
Designing Data-Intensive Applications by Martin Kleppmann
Everybody Lies by Seth Stephens-Davidowitz
Data Smart by John W. Foreman
Understanding Machine Learning by Shai Shalev-Shwartz
Weapons of Math Destruction by Cathy O'Neil
Python Machine Learning by Sebastian Raschka
Data Science and Big Data Analytics by Emc Education Services
Numsense! Data Science for the Layman by Annalyn Ng
Machine Learning by Tom M. Mitchell
Fundamentals of Machine Learning for Predictive Data Analytics by John D. Kelleher
Predictive Analytics by Eric Siegel
Practical Data Science with R by Nina Zumel
The Master Algorithm by Pedro Domingos
Big Data by Nathan Marz
Think Stats by Allen B. Downey
Big Data For Dummies by Judith S. Hurwitz
Life 3.0 by Max Tegmark
Superintelligence by Nick Bostrom
The Big Data-Driven Business by Russell Glass
The Data Science Handbook by Carl Shan
The Art of Data Science by Roger Peng
Machine Learning For Dummies by John Paul Mueller
Machine Learning for Hackers by Drew Conway
Machine Learning by Ethem Alpaydin
The Singularity Is Near by Ray Kurzweil
Reinforcement Learning by Richard S. Sutton
Make Your Own Neural Network by Tariq Rashid
R Cookbook by Paul Teetor
Hadoop by Tom White
Business unIntelligence by Barry Devlin
Machine Learning in Action by Peter Harrington
Analytics in a Big Data World by Bart Baesens
Big Data in Practice by Bernard Marr
Big Data, Data Mining, and Machine Learning by Jared Dean
Big Data For Beginners by Vince Reynolds
The Art of Statistics by David Spiegelhalter
Applied Predictive Modeling by Max Kuhn
Grokking Deep Learning by Andrew Trask
From Big Data to Big Profits by Russell Walker
Learning Spark by Holden Karau
Data Science Job by Przemek Chojecki
Data Architecture by W. H. Inmon
Artificial Intelligence by Melanie Mitchell
Machine Learning with R by Brett Lantz
How to Create a Mind by Ray Kurzweil
Mining of Massive Datasets by Jure Leskovec
Natural Language Processing with Python by Steven Bird
The Human Face of Big Data by Rick Smolan
Prediction Machines by Ajay Agrawal
The Data Science Handbook by Field Cady
Automate This by Christopher Steiner
Data Strategy by Bernard Marr
Machine Learning by Peter Flach
Human + Machine by Paul R. Daugherty
Inflection Point by Scott Stawski
Smart Cities by Anthony M. Townsend
Algorithms of Oppression by Safiya Noble
An Introduction to Probability Theory and Its Applications by William Feller
Data Mining by Ian H. Witten
Artificial Intelligence for Humans by Jeff Heaton
Numbersense by Kaiser Fung
Invisible Women by Caroline Criado Perez
Automate the Boring Stuff with Python by Al Sweigart
Lean Analytics by Alistair Croll
Paradigms of Artificial Intelligence Programming by Peter Norvig
Generative Deep Learning by David Foster
Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili
Information Theory, Inference and Learning Algorithms by David J. C. MacKay
Think Python by Allen B. Downey
Python Cookbook by David Beazley
Advanced R by Hadley Wickham
Fundamentals of Data Visualization by Claus O. Wilke
Build a Career in Data Science by Emily Robinson, Jacqueline Nolis
Making Big Data Work for Your Business by Sudhi Sinha