# 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.

100 books on the list

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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Concepts, Tools, and Techniques to Build Intelligent Systems

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...

Appears in 28 articles

Deep Learning

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...

Appears in 23 articles

Pattern Recognition and Machine Learning

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,...

Appears in 18 articles

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...

Appears in 16 articles

The Elements of Statistical Learning

Data Mining, Inference, and Prediction (Springer Series in Statistics)

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...

Appears in 15 articles

Deep Learning with Python

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 ...

Appears in 14 articles

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 ...

Appears in 14 articles

An Introduction to Statistical Learning

with Applications in R (Springer Texts in Statistics)

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...

Appears in 14 articles

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,...

Appears in 14 articles

Machine Learning For Absolute Beginners

A Plain English Introduction (Machine Learning From Scratch)

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...

Appears in 13 articles

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...

Appears in 12 articles

Recommended by

Lex FridmanThe 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

Everybody Lies by Seth Stephens-Davidowitz

Hadoop by Tom White

Weapons of Math Destruction by Cathy O'Neil

The Master Algorithm by Pedro Domingos

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

R Cookbook by Paul Teetor

Python Machine Learning - Second Edition by Sebastian Raschka

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

The Book of Why by Judea Pearl

The Fourth Age by Byron Reese

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

Hadoop Operations by Eric Sammer

Introduction to Probability by Charles M. Grinstead

Mapreduce Design Patterns by Adam Shook

Bayesian Reasoning and Machine Learning by David Barber

Building Machine Learning Powered Applications by Emmanuel Ameisen

TinyML by Pete Warden

Data Science by John D. Kelleher

Advanced Analytics with Spark by Sandy Ryza

Head First Statistics by Dawn Griffiths