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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 92 articles, Barack Obama, Bill Gates, Elon Musk and 32 others.
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Concepts, Tools, and Techniques to Build Intelligent Systems
Aurélien Géron - Oct 14, 2019
Goodreads Rating
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two pro...
Deep Learning
Ian Goodfellow - Nov 17, 2016
Goodreads Rating
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the comp...
Pattern Recognition and Machine Learning
Christopher M. Bishop - Aug 16, 2006
Goodreads Rating
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream,...
Big Data
A Revolution That Will Transform How We Live, Work, and Think
Viktor Mayer-Schönberger - Mar 04, 2014
Goodreads Rating
A revelatory exploration of the hottest trend in technology and the dramatic impact it will have on the economy, science, and society at large.Which paint color is most likely to tell you that a used car is in good shape? How can officials identify the most dangerous New York City manholes before they explode? And how did Google searches predict th...
The Elements of Statistical Learning
Data Mining, Inference, and Prediction (Springer Series in Statistics)
Trevor Hastie - Jul 29, 2003
Goodreads Rating
During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data...
Deep Learning with Python
François Chollet - Dec 21, 2017
Goodreads Rating
Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on ...
Introduction to Machine Learning with Python
A Guide for Data Scientists
Andreas C. Müller - Oct 25, 2016
Goodreads Rating
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine ...
An Introduction to Statistical Learning
with Applications in R (Springer Texts in Statistics)
Gareth James - Jun 24, 2013
Goodreads Rating
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modelin...
Python for Data Analysis
Data Wrangling with Pandas, NumPy, and IPython
Wes McKinney - Oct 24, 2017 (first published in 2011)
Goodreads Rating
Looking for complete instructions on manipulating, processing, cleaning, and crunching structured data in Python? The second edition of this hands-on guide--updated for Python 3.5 and Pandas 1.0--is packed with practical cases studies that show you how to effectively solve a broad set of data analysis problems, using Python libraries such as NumPy,...
Machine Learning For Absolute Beginners
A Plain English Introduction (Machine Learning From Scratch)
Oliver Theobald - Jan 01, 2018
Goodreads Rating
Featured by Tableau as the first of "7 Books About Machine Learning for Beginners" Ready to crank up a virtual server and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?Well, hold on there...Before you embark on your epic journey into the world of machine learning, there is some theory and statistical princ...
Artificial Intelligence
A Modern Approach (4th Edition)
Stuart Russell - May 08, 2020 (first published in 1994)
Goodreads Rating
The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence.The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in...
The Hundred-Page Machine Learning Book by Andriy Burkov
The Signal and the Noise by Nate Silver
Big Data at Work by Thomas H. Davenport
Data Science for Business by Foster Provost
Machine Learning by Kevin P. Murphy
Naked Statistics by Charles Wheelan
Doing Data Science by Cathy O'Neil
Programming Collective Intelligence by Segaran
Data Science from Scratch by Joel Grus
Too Big to Ignore by Phil Simon
Designing Data-Intensive Applications by Martin Kleppmann
Storytelling with Data by Cole Nussbaumer Knaflic
R for Data Science by Hadley Wickham
Understanding Machine Learning by Shai Shalev-Shwartz
Machine Learning by Tom M. Mitchell
Big Data by Nathan Marz
Fundamentals of Machine Learning for Predictive Data Analytics by John D. Kelleher
The Big Data-Driven Business by Russell Glass
Practical Statistics for Data Scientists by Peter Bruce
Machine Learning For Dummies by John Paul Mueller
Machine Learning for Hackers by Drew Conway
Predictive Analytics by Eric Siegel
Python Machine Learning by Sebastian Raschka
Python Data Science Handbook by Jake VanderPlas
Data Smart by John W. Foreman
Data Science and Big Data Analytics by EMC Education Services
The Data Science Handbook by Carl Shan
Hadoop by Tom White
Everybody Lies by Seth Stephens-Davidowitz
The Master Algorithm by Pedro Domingos
Weapons of Math Destruction by Cathy O'Neil
Big Data For Dummies by Judith S. Hurwitz
Life 3.0 by Max Tegmark
The Singularity Is Near by Ray Kurzweil
Make Your Own Neural Network by Tariq Rashid
From Big Data to Big Profits by Russell Walker
Superintelligence by Nick Bostrom
The Art of Data Science by Roger Peng
Analytics in a Big Data World by Bart Baesens
Machine Learning in Action by Peter Harrington
Machine Learning by Ethem Alpaydin
Data Science For Dummies by Lillian Pierson
Applied Predictive Modeling by Max Kuhn
Machine Learning with R by Brett Lantz
Python Machine Learning - Second Edition by Sebastian Raschka
R Cookbook by Paul Teetor
Reinforcement Learning by Richard S. Sutton
The Human Face of Big Data by Rick Smolan
Machine Learning by Peter Flach
Learning Spark by Holden Karau
Data Strategy by Bernard Marr
Data Mining by Ian H. Witten
Generative Deep Learning by David Foster
Automate the Boring Stuff with Python by Al Sweigart
Advanced R by Hadley Wickham
Python Cookbook by David Beazley
Advances in Financial Machine Learning by Marcos Lopez de Prado
Mining of Massive Datasets by Jure Leskovec
The Big Book of Dashboards by Steve Wexler
An Introduction to Probability Theory and Its Applications by William Feller
Machine Learning with Python Cookbook by Chris Albon
Grokking Deep Learning by Andrew Trask
Making Big Data Work for Your Business by Sudhi Sinha
Bayesian Methods for Hackers by Cameron Davidson-Pilon Davidson-Pilon
Practical Data Science with R by Nina Zumel
Natural Language Processing with Python by Steven Bird
How to Create a Mind by Ray Kurzweil
People Analytics in the Era of Big Data by Jean Paul Isson
Hadoop in Practice by Alex Holmes
Prediction Machines by Ajay Agrawal
Automate This by Christopher Steiner
AI Superpowers by Kai-Fu Lee
Paradigms of Artificial Intelligence Programming by Peter Norvig
Deep Reinforcement Learning Hands-On by Maxim Lapan
Information Theory, Inference and Learning Algorithms by David J. C. MacKay
Numsense! Data Science for the Layman by Annalyn Ng
Speech and Language Processing by Daniel Jurafsky
Hands-On Programming with R by Garrett Grolemund
Building Machine Learning Powered Applications by Emmanuel Ameisen
TinyML by Pete Warden
Hadoop Operations by Eric Sammer
Introduction to Probability by Charles M. Grinstead
Mapreduce Design Patterns by Adam Shook
The Book of Why by Judea Pearl
Bayesian Reasoning and Machine Learning by David Barber
The Fourth Age by Byron Reese
Data Science by John D. Kelleher
Advanced Analytics with Spark by Sandy Ryza
Head First Statistics by Dawn Griffiths