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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 104 articles, Barack Obama, Bill Gates, Elon Musk and 44 others.
Best Data Science Books
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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 - 2019-10-14
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 book cover
Deep Learning
Ian Goodfellow - 2016-11-17
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...
Introduction to Machine Learning with Python book cover
Introduction to Machine Learning with Python
Andreas Muller - 2016-11-10 (first published in 2015)
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 ...
Big Data book cover
Big Data
A Revolution That Will Transform How We Live, Work, and Think
Viktor Mayer-schönberger - 2014-03-04
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...
Pattern Recognition and Machine Learning book cover
Pattern Recognition and Machine Learning
Christopher M. Bishop - 2006-08-16
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,...
The Elements of Statistical Learning book cover
The Elements of Statistical Learning
Data Mining, Inference, and Prediction (Springer Series in Statistics)
Trevor Hastie - 2003-07-29
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 book cover
Deep Learning with Python
François Chollet - 2017-12-21
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 ...
Python for Data Analysis book cover
Python for Data Analysis
Data Wrangling with Pandas, NumPy, and IPython
Wes McKinney - 2017-10-24 (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,...
Naked Statistics book cover
Naked Statistics
Stripping the Dread from the Data
Charles Wheelan - 2013-01-07
Goodreads Rating
Once considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called “sexy.” From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on...
Data Science from Scratch book cover
Data Science from Scratch
First Principles with Python
Joel Grus - 2019-05-15
Goodreads Rating
To really learn data science, you should not only master the tools--data science libraries, frameworks, modules, and toolkits--but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.If you h...
Recommended by
Thorsten Heller
Doing Data Science by Cathy O'Neil
An Introduction to Statistical Learning by Gareth James
Data Science for Business by Foster Provost
Machine Learning For Absolute Beginners by Oliver Theobald
Artificial Intelligence by Stuart Russell
The Hundred-Page Machine Learning Book by Andriy Burkov
R for Data Science by Hadley Wickham
The Signal and the Noise by Nate Silver
Python Data Science Handbook by Jake Vanderplas
Big Data at Work by Thomas H. Davenport
Designing Data-Intensive Applications by Martin Kleppmann
Storytelling with Data by Cole Nussbaumer Knaflic
Python Machine Learning by Sebastian Raschka
Machine Learning by Kevin P. Murphy
Programming Collective Intelligence by Segaran
Data Smart by John W. Foreman
Too Big to Ignore by Phil Simon
Fundamentals of Machine Learning for Predictive Data Analytics by John D. Kelleher
Understanding Machine Learning by Shai Shalev-Shwartz
Machine Learning by Tom M. Mitchell
Everybody Lies by Seth Stephens-Davidowitz
Big Data by Nathan Marz
Predictive Analytics by Eric Siegel
Data Science For Dummies by Lillian Pierson
The Big Data-Driven Business by Russell Glass
The Data Science Handbook by Carl Shan
Data Science and Big Data Analytics by Emc Education Services
Weapons of Math Destruction by Cathy O'Neil
The Art of Data Science by Roger Peng
Machine Learning For Dummies by John Paul Mueller
Machine Learning for Hackers by Drew Conway
Big Data For Dummies by Judith S. Hurwitz
R Cookbook by Paul Teetor
Practical Data Science with R by Nina Zumel
Hadoop by Tom White
The Master Algorithm by Pedro Domingos
Life 3.0 by Max Tegmark
The Singularity Is Near by Ray Kurzweil
Numsense! Data Science for the Layman by Annalyn Ng
From Big Data to Big Profits by Russell Walker
Superintelligence by Nick Bostrom
Make Your Own Neural Network by Tariq Rashid
Analytics in a Big Data World by Bart Baesens
Machine Learning in Action by Peter Harrington
Machine Learning by Ethem Alpaydin
Applied Predictive Modeling by Max Kuhn
Machine Learning with R by Brett Lantz
Grokking Deep Learning by Andrew Trask
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
The Data Science Handbook by Field Cady
Generative Deep Learning by David Foster
The Book of Why by Judea Pearl
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
Fundamentals of Data Visualization by Claus O. Wilke
An Introduction to Probability Theory and Its Applications by William Feller
Making Big Data Work for Your Business by Sudhi Sinha
Natural Language Processing with Python by Steven Bird
Automate the Boring Stuff with Python by Al Sweigart
How to Create a Mind by Ray Kurzweil
Data Science by John D. Kelleher
People Analytics in the Era of Big Data by Jean Paul Isson
Machine Learning with Python Cookbook by Chris Albon
Bayesian Methods for Hackers by Cameron Davidson-Pilon Davidson-Pilon
Hadoop in Practice by Alex Holmes
Prediction Machines by Ajay Agrawal
AI Superpowers by Kai-fu Lee
Deep Reinforcement Learning Hands-On by Maxim Lapan
The Fourth Age by Byron Reese
Information Theory, Inference and Learning Algorithms by David J. C. MacKay
R Graphics Cookbook by Winston Chang
Hands-On Programming with R by Garrett Grolemund
R for Everyone by Jared P. Lander
Introduction to Probability by Charles M. Grinstead
Bayesian Reasoning and Machine Learning by David Barber
Paradigms of Artificial Intelligence Programming by Peter Norvig
Deep Learning Illustrated by Jon Krohn
Building Machine Learning Powered Applications by Emmanuel Ameisen
TinyML by Pete Warden
Speech and Language Processing by Daniel Jurafsky
Python for Data Science for Dummies by John Paul Mueller, Luca Massaron