100 Best Data Science Books of All Time

One of Wall Street Journal's Best Ten Works of Nonfiction in 2012

New York Times Bestseller

"Not so different in spirit from the way public intellectuals like John Kenneth Galbraith once shaped discussions of economic policy and public figures like Walter Cronkite helped sway opinion on the Vietnam War…could turn out to be one of the more momentous books of the decade."
-New York Times Book Review

"Na

Recommended by Bill Gates.

CEO/Microsoft

Anyone interested in politics may be attracted to Nate Silver’s The Signal and the Noise: Why So Many Predictions Fail—but Some Don't. Silver is the New York Times columnist who got a lot of attention last fall for predicting—accurately, as it turned out–the results of the U.S. presidential election. This book actually came out before the election, though, and it’s about predictions in many domains besides politics. Silver knows a lot about baseball, and I especially liked his explanation of hold’em poker. A few pages – where he talks about how early computers supposedly made everything less efficient—are utter nonsense. I wish he had gone into more depth on some things, like why it is that voters are increasingly polarized. I liked the book, though I wish he’d gone deeper on a number of topics. [source]

9

Factfulness: Ten Reasons We're Wrong About the World – and Why Things Are Better Than You Think

Hans Rosling

4.8

Factfulness: The stress-reducing habit of only carrying opinions for which you have strong supporting facts.

When asked simple questions about global trends—what percentage of the world’s population live in poverty; why the world’s population is increasing; how many girls finish school—we systematically get the answers wrong. So wrong that a chimpanzee choosing answers at random will consistently o

Recommended by Barack Obama, Bill Gates, Aidan Connolly, and 6 others.

Barack Obama

Former USA President

As 2018 draws to a close, I’m continuing a favorite tradition of mine and sharing my year-end lists. It gives me a moment to pause and reflect on the year through the books I found most thought-provoking, inspiring, or just plain loved. It also gives me a chance to highlight talented authors – some who are household names and others who you may not have heard of before. Here’s my best of 2018 list. [source]

CEO/Microsoft

This was a breakthrough to me. The framework Hans enunciates is one that took me decades of working in global development to create for myself, and I could have never expressed it in such a clear way. I’m going to try to use this model moving forward. [source]

Aidan Connolly

I would suggest Hans Rosling’s Factfulness on the real world of statistics. [source]

10

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

Cathy O'Neil

4.8

Longlisted for the National Book Award
New York Times Bestseller

A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life -- and threaten to rip apart our social fabric

We live in the age of the algorithm. Increasingly, the decisions that affect our lives--where we go to school, whether we get a car loan, how much we pay for health insurance--are being made not

Recommended by Paula Boddington and Ramesh Srinivasan.

Paula Boddington

How the use of algorithms has affected people’s lives and occasionally ruined them. [source]

Ramesh Srinivasan

This book is a really fantastic analysis of how quantification, the collection of data, the modelling around data, the predictions made by using data, the algorithmic and quantifiable ways of predicting behaviour based on data, are all built by elites for elites and end up, quite frankly, screwing over everybody else. [source]

11

Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are

Seth Stephens-Davidowitz

4.7

Foreword by Steven Pinker

Blending the informed analysis of The Signal and the Noise with the instructive iconoclasm of Think Like a Freak, a fascinating, illuminating, and witty look at what the vast amounts of information now instantly available to us reveals about ourselves and our world—provided we ask the right questions.

By the end of an average day in the early twenty-first century, human bei

Recommended by Jj. Omojuwa and Ron Fournier.

Jj. Omojuwa

@SympLySimi Lol. Read this book. You’d love it. https://t.co/d2cLOyoiZ9 [source]

Ron Fournier

Just finished, “Everybody Lies” by @SethS_D, which in addition to being a tremendous education on Big Data, includes the best conclusion to a non-fiction book I’ve ever read. Read it. -30- [source]

12

Moneyball: The Art of Winning an Unfair Game

Michael Lewis

4.7

Moneyball is a quest for something as elusive as the Holy Grail, something that money apparently can't buy: the secret of success in baseball. The logical places to look would be the front offices of major league teams and the dugouts, perhaps even in the minds of the players themselves. Michael Lewis mines all these possibilities - his intimate and original portraits of big league ballplayers are

Recommended by Ev Williams, Azeem Azhar, Carol Dweck, and 5 others.

Carol Dweck

You would think that the relationship between training and skill would be utterly obvious in sports, but apparently it isn’t. [source]

Daniel Hamermesh

What the book is really about is a guy who is using some econometrics to predict which players will do better in advancing wins, which is a remarkable use of economic thinking. [source]

David Papineau

It’s a parable of the disinclination of people in general to base their practices on evidence, a parable for evidence-based policy in general. [source]

13

Naked Statistics: Stripping the Dread from the Data

Charles Wheelan

4.7

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 standardized tests? How does Netflix know which m

14

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Geron

4.7

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 production-ready Python frameworks-scikit-learn and

Recommended by Mark Tabladillo.

Mark Tabladillo

Book to Start You on Machine Learning - KDnuggets https://t.co/19fdX59b0d This book is “Hands-On Machine Learning with Scikit-Learn & TensorFlow”. each new revision has become an even better version of one of the best in-depth resources to learn Machine Learning by doing. https://t.co/ujyUH3xU3e [source]

15

The Visual Display of Quantitative Information

Edward R. Tufte

4.7

The classic book on statistical graphics, charts, tables. Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relat

Recommended by Bret Victor and Michael Okuda.

Michael Okuda

Edward Tufte's classic book, The Visual Display of Quantitative Information is a fascinating, surprisingly readable treatise for anyone interested in infographics. When I hired artists for the Star Trek graphics dept, I sometimes asked them to read it.https://t.co/cK4GQqBDxp [source]

16

The Black Swan: The Impact of the Highly Improbable

Nassim Nicholas Taleb

4.6

A black swan is a highly improbable event with three principal characteristics: It is unpredictable; it carries a massive impact; and, after the fact, we concoct an explanation that makes it appear less random, and more predictable, than it was.

The astonishing success of Google was a black swan; so was 9/11. For Nassim Nicholas Taleb, black swans underlie almost everything about our world, from t

Recommended by Bill Gates, Jeff Bezos, Peter Thiel, and 18 others.

CEO/Microsoft

On Bill Gates's reading list in 2012. [source]

Jeff Bezos

Founder/Amazon

From the book "The Everything Store: and the Age of Amazon" “The scholar argues that people are wired to see patterns in chaos while remaining blind to unpredictable events, with massive consequences. Experimentation and empiricism trumps the easy and obvious narrative,” Stone writes. [source]

Founder/StockPickr

And throw in “The Black Swan” and “Fooled by Randomness”. “Fragile” means if you hit something might break. “Resilient” means if you hit something, it will stay the same. On my podcast Nassim discusses “Antifragility” – building a system, even on that works for you on a personal level, where you if you harm your self in some way it becomes stronger. That podcast changed my life He discusses Antifragility throughout history, up to our current economic situation, and even in our personal situations. [source]

17

Thinking, Fast and Slow

Kahneman

4.6

Major New York Times bestseller
Winner of the National Academy of Sciences Best Book Award in 2012
Selected by the New York Times Book Review as one of the best books of 2011
A Globe and Mail Best Books of the Year 2011 Title
One of The Economist's 2011 Books of the Year
One of The Wall Street Journal's Best Nonfiction Books of the Year 2011
2013 Presidential Medal of Freedom Recipient

In the internatio

Recommended by Barack Obama, Bill Gates, Ev Williams, and 45 others.

Barack Obama

Former USA President

A few months ago, Mr. Obama read “Thinking, Fast and Slow,” by Daniel Kahneman, about how people make decisions — quick, instinctive thinking versus slower, contemplative deliberation. For Mr. Obama, a deliberator in an instinctive business, this may be as instructive as any political science text. [source]

CEO/Microsoft

On Bill Gates's reading list in 2012. [source]

Marc Andreessen

Co-Founder/Andreessen Horowitz

Captivating dive into human decision making, marred by inclusion of several/many? psychology studies that fail to replicate. Will stand as a cautionary tale? [source]

18

Freakonomics: A Rogue Economist Explores the Hidden Side of Everything

Steven D. Levitt

4.6

Which is more dangerous, a gun or a swimming pool? What do schoolteachers and sumo wrestlers have in common? Why do drug dealers still live with their moms? How much do parents really matter? What kind of impact did Roe v. Wade have on violent crime? Freakonomics will literally redefine the way we view the modern world.

These may not sound like typical questions for an economist to ask. But Steven

Recommended by Malcolm Gladwell, Daymond John, Ev Williams, and 12 others.

Malcolm Gladwell

Writer & Journalist

I don’t need to say much here. This book invented an entire genre. Economics was never supposed to be this entertaining. [source]

Daymond John

Founder/FUBU

I love newer books like this book. [source]

Founder/StockPickr

James Altucher recommended this book on the podcast "The Tim Ferriss Show". [source]

19

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Trevor Hastie, Robert Tibshirani, Jerome Friedman

4.6

During the past decade there has been an explosion in computation and information technology. With it have 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 mining, machine learning, and bioinformatics. Ma

Recommended by Nassim Nicholas Taleb.

Nassim Nicholas Taleb

Author

Very comprehensive, sufficiently technical to get most of the plumbing behind machine learning. Very useful as a reference book (actually, there is no other complete reference book). The authors are the real thing (Tibshirani is the one behind the LASSO regularization technique). Uses some mathematical statistics without the burdens of measure theory and avoids the obvious but complicated proofs. I own two copies of this edition, one for the office, one for my house, and the authors generously provide the PDF for travelers like me. [source]

20

Pattern Recognition and Machine Learning

Christopher M. Bishop

4.6

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, while graphical models have emerged as a general

21

Practical Statistics for Data Scientists: 50 Essential Concepts

Peter Bruce and Andrew Bruce

4.5

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.

Many

22

Deep Learning

Ian Goodfellow, Yoshua Bengio, et al.

4.5

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 computer gathers knowledge from experience, there is no

Recommended by Elon Musk, Nassim Nicholas Taleb, Satya Nadella, and 2 others.

Elon Musk

Founder/SpaceX

Written by three experts in the field, Deep Learning is the only comprehensive book on the subject. [source]

Nassim Nicholas Taleb

Author

Very clear exposition, does the math without getting lost in the details. Although many of the concepts of the introductory first 100 pages can be found elsewhere, they are presented with remarkable cut-to-the-chase clarity. [source]

Satya Nadella

CEO/Microsoft

Elon Musk and Facebook AI chief Yann LeCun have praised this textbook on one of software’s most promising frontiers. After its publication, Microsoft signed up coauthor Bengio, a pioneer in machine learning, as an adviser [source]

23

Doing Data Science

Cathy O'Neil, Rachel Schutt

4.5

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.

In many of these chapter-lon

24

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

Hadley Wickham and Garrett Grolemund

4.5

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.

Authors Hadley Wic

25

Lean Analytics: Use Data to Build a Better Startup Faster

Alistair Croll and Benjamin Yoskovitz

4.5

Whether you’re a startup founder trying to disrupt an industry or an intrapreneur trying to provoke change from within, your biggest challenge is creating a product people actually want. Lean Analytics steers you in the right direction.

This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and how to spread the word. Pa

Recommended by Ash Maurya, Marty Cagan, Ola Olusoga, and 4 others.

Ola Olusoga

Like Charlie Munger once said: “I’ve long believed that a certain system - which almost any intelligent person can learn - works way better than the systems most people use to understand the world. What you need is a latticework of mental models in your head. And, with that system, things gradually fit together in a way that enhances cognition. Just as multiple factors shape every system, multiple mental models from a variety of disciplines are necessary to understand that system". You can read this book to start building a "latticework of mental models in your head". [source]

26

The Hundred-Page Machine Learning Book

Andriy Burkov

4.5

WARNING! To avoid buying counterfeit on Amazon, click on "See All Buying Options" and choose "Amazon.com" and not a third-party seller.

Concise and to the point — the book can be read during a week. During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.

Companion wiki — the book has a conti

Recommended by Kirk Borne.

Kirk Borne

Recent top-selling books in #AI & #MachineLearning: https://t.co/Ij9I7SzR4d ————— #BigData #DataScience #DataMining #Algorithms #PredictiveAnalytics #Python ————— ...in the TOP 10: 1)The Hundred-Page ML Book: https://t.co/dQ7nP6gwP0 2)Hands-on ML with...: https://t.co/Y0Iz3GbtGP https://t.co/72rAFN1FwW [source]

27

Applied Predictive Modeling

Max Kuhn and Kjell Johnson

4.5

This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic

Recommended by Kirk Borne.

Kirk Borne

Find more than 40 useful #PredictiveModeling articles here at @DataScienceCtrl https://t.co/KdcvLRffRk #abdsc ———— #BigData #DataScience #AI #MachineLearning #Forecasting #Statistics #PredictiveAnalytics ——— +This is the best book on the subject: https://t.co/SmsepmniHi https://t.co/amBJHCJSHN [source]

28

Deep Learning with Python

François Chollet

4.4

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 your smartphone, and much more.

In particular, Deep

29

Dataclysm: Who We Are (When We Think No One's Looking)

Christian Rudder

4.4

A New York Times Bestseller

An audacious, irreverent investigation of human behavior—and a first look at a revolution in the making

Our personal data has been used to spy on us, hire and fire us, and sell us stuff we don’t need. In Dataclysm, Christian Rudder uses it to show us who we truly are.

For centuries, we’ve relied on polling or small-scale lab experiments to study human behavior. Today, a

Recommended by Elad Yom-Tov.

Elad Yom-Tov

Christian Rudder was the chief scientist of a dating website, OK Cupid. [source]

30

Algorithms to Live By: The Computer Science of Human Decisions

Brian Christian and Griffiths

4.4

A fascinating exploration of how insights from computer algorithms can be applied to our everyday lives, helping to solve common decision-making problems and illuminate the workings of the human mind

All our lives are constrained by limited space and time, limits that give rise to a particular set of problems. What should we do, or leave undone, in a day or a lifetime? How much messiness should we

Recommended by Doug McMillon, Chris Oliver, Emi Gal, and 2 others.

Doug McMillon

CEO/Walmart

Here are some of my favorite reads from 2017. Lots of friends and colleagues send me book suggestions and it's impossible to squeeze them all in. I continue to be super curious about how digital and tech are enabling people to transform our lives but I try to read a good mix of books that apply to a variety of areas and stretch my thinking more broadly. [source]

Chris Oliver

This is a great book talking about how you can use computer science to help you make decisions in life. How do you know when to make a decision on the perfect house? Car? etc? It helps you apply algorithms to making those decisions optimally without getting lost. [source]

Emi Gal

When asked what books he would recommend to young people interested in his career path, mentioned Algorithms to Live By. [source]

31

Machine Learning: A Probabilistic Perspective

Kevin P. Murphy

4.4

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehen

Recommended by Kirk Borne.

Kirk Borne

#MachineLearning — a Probabilistic Perspective: https://t.co/wAZwLoUFGF ———— #BigData #Statistics #DataScience #DeepLearning #AI #Algorithms #StatisticalLiteracy #Mathematics #abdsc ——— ⬇Get this brilliant 1100-page 28-chapter highly-rated book: https://t.co/Tm2zchpHSu https://t.co/jprUDdzkj8 [source]

32

Big Data: A Revolution That Will Transform How We Live, Work, and Think

Viktor Mayer-Schönberger, Kenneth Cukier

4.4

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 the spread of the H1N1 flu outbreak?

The key to answe

33

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition

Sebastian Raschka

4.4

Link to the GitHub Repository containing the code examples and additional material: https://github.com/rasbt/python-machi...

Many of the most innovative breakthroughs and exciting new technologies can be attributed to applications of machine learning. We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn thi

34

Superforecasting: The Art and Science of Prediction

Philip E. Tetlock, Dan Gardner

4.3

A New York Times Bestseller

An Economist Best Book of 2015

"The most important book on decision making since Daniel Kahneman's Thinking, Fast and Slow."
—Jason Zweig, The Wall Street Journal

Everyone would benefit from seeing further into the future, whether buying stocks, crafting policy, launching a new product, or simply planning the week’s meals. Unfortunately, people tend to be terrible foreca

Recommended by Vinod Khosla, Vinod Khosla, Julia Galef, and 1 others.

Julia Galef

Has some good advice on how to improve your ability to make accurate predictions. [source]

Sheil Kapadia

Read the book Superforecasting, had a great conversation with @bcmassey and came up with seven ideas for how NFL teams can try to find small edges during the draft process. Would love to hear feedback on this one. https://t.co/PdN1fKCagl [source]

35

Designing Data-Intensive Applications

Martin Kleppmann

4.3

Many forces affect software today: larger datasets, geographical disparities, complex company structures, and the growing need to be fast and nimble in the face of change.

Proven approaches such as service-oriented and event-driven architectures are joined by newer techniques such as microservices, reactive architectures, DevOps, and stream processing. Many of these patterns are successful by thems

36

Envisioning Information

Edward R. Tufte

4.3

The celebrated design professor here tackles the question of how best to communicate real-life experience in a two-degree format, whether on the printed page or the computer screen. The Whole Earth Review called Envisioning Information a "passionate, elegant revelation."

Recommended by Kevin Rose, Jeff Atwood, Bret Victor, and 1 others.

Kevin Rose

Co-founder/Digg

The master when it comes to taking complicated data and turning it into beautiful charts and graphs that are easy to understand. If you’re into graphic design, print design, web design, you name it, you’re going to get some really good information and how tos out of these books. He has a whole series of these books. [source]

37

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

Eric Siegel

4.3

"Mesmerizing & fascinating..." -- The Seattle Post-Intelligencer

"The Freakonomics of big data." --Stein Kretsinger, founding executive of Advertising.com

Award-winning - Used by over 30 universities - Translated into 9 languages

An introduction for everyone. In this rich, fascinating -- surprisingly accessible -- introduction, leading expert Eric Siegel reveals how predictive analytics (aka machi

38

Introduction to Machine Learning with Python: A Guide for Data Scientists

Andreas C. Müller and Sarah Guido

4.3

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 learning applications are limited only by your ima

Recommended by Francesco Marconi.

Francesco Marconi

Top programming languages ranked by its annual search engine popularity. Python has gained momentum because of its importance to machine learning development. At @WSJ we are using it to build tools for journalists. Tip: this is a great book for anyone who wants to get started! https://t.co/ZsHjqB5gvC [source]

39

Data Science with R

Hadley Wickham and Garrett Grolemund

4.3

Recommended by Tim @Realscientists.

Tim @Realscientists

If you are interested in learning programming, there are lots of great tutorials. For data analysis, R and the R 4 data science book is a great way to go https://t.co/zezYpG0TRL, and for general R syntax, there is the swirl learning package https://t.co/Tzfpnlgo3O /20 [source]

40

The Truthful Art: Data, Charts, and Maps for Communication

Alberto Cairo

4.3

No matter what your actual job title, you are--or soon will be--a data worker.
Every day, at work, home, and school, we are bombarded with vast amounts of free data collected and shared by everyone and everything from our co-workers to our calorie counters. In this highly anticipated follow-up to The Functional Art--Alberto Cairo's foundational guide to understanding information graphics and visua

41

Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists

Philipp K. Janert

4.3

Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those i

42

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

Pedro Domingos

4.3

A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own
In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil

Recommended by Vinod Khosla.

Vinod Khosla

Co-Founder/Sun Microsystem

If you want speculation about what the master AI might need (one view). For a slightly more technical read, I’d suggest Ian Goodfellows Deep Learning. [source]

43

Invisible Women: Data Bias in a World Designed for Men

Caroline Criado Perez

4.3

Imagine a world where your phone is too big for your hand, where your doctor prescribes a drug that is wrong for your body, where in a car accident you are 47% more likely to be seriously injured, where every week the countless hours of work you do are not recognised or valued. If any of this sounds familiar, chances are that you're a woman.

Invisible Women shows us how, in a world largely built fo

Recommended by Adam Wagner, Asher Wolf, Caitlin Moran, and 9 others.

Adam Wagner

@CCriadoPerez @MumsnetTowers I love this - the book, Poppy, the mug - it's so well composed! [source]

Asher Wolf

Tell me your best thing today. Mine was an amazing book and greeting card arrived in the post from the wonderful @MmeEmmeline https://t.co/GpEs2lg037 [source]

Caitlin Moran

@CCriadoPerez mate, I bow in admiration. it's an AMAZING, game-changing book. [source]

44

Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets

Nassim Nicholas Taleb

4.3

Fooled by Randomness is a standalone book in Nassim Nicholas Taleb’s landmark Incerto series, an investigation of opacity, luck, uncertainty, probability, human error, risk, and decision-making in a world we don’t understand. The other books in the series are The Black Swan, Antifragile, and The Bed of Procrustes.

Now in a striking new hardcover edition, Fooled by Randomness is the word-of-mouth se

Recommended by Malcolm Gladwell, Ev Williams, James Altucher, and 6 others.

Founder/StockPickr

And throw in “The Black Swan” and “Fooled by Randomness”. “Fragile” means if you hit something might break. “Resilient” means if you hit something, it will stay the same. On my podcast Nassim discusses “Antifragility” – building a system, even on that works for you on a personal level, where you if you harm your self in some way it becomes stronger. That podcast changed my life He discusses Antifragility throughout history, up to our current economic situation, and even in our personal situations. [source]

Anant Jain

The five-book series, "Incerto", by Nassim Nicholas Taleb has had a profound impact on how I think about the world. There’s some overlap across the books — but you'll likely find the repetition helpful in retaining the content better. [source]

Art De Vany

I used this book is a class I taught on the economics of uncertainty. [source]

45

Programming Collective Intelligence: Building Smart Web 2.0 Applications

Toby Segaran

4.2

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect da

46

The Art of Data Science: A Guide for Anyone Who Works with Data

Roger Peng and Elizabeth Matsui

4.2

This book describes, simply and in general terms, the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners

47

Advanced R

Hadley Wickham

4.2

An Essential Reference for Intermediate and Advanced R Programmers

Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R.

The book develops the necessary skills to produce qualit

48

Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems

Aurélien Géron

4.2

49

The Book of Why: The New Science of Cause and Effect

Judea Pearl

4.2

A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence
"Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleag

Recommended by D.a. Wallach and Kirk Borne.

D.a. Wallach

@EricTopol @yudapearl @bschoelkopf @MPI_IS I love @yudapearl 's book so much! Profound, heterodox. [source]

Kirk Borne

.@yudapearl wrote the awesome "Book of Why", but he recommends this fun and less #mathematics-heavy read >> his #AI lecture given in 1999: https://t.co/kNYIoJ8qcY #DataScience #MachineLearning #Statistics #BookofWhy #Causalinference #Bayes https://t.co/CNQlKP8cU3 [source]

50

Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World

Bruce Schneier

4.2

Your cell phone provider tracks your location and knows who’s with you. Your online and in-store purchasing patterns are recorded, and reveal if you're unemployed, sick, or pregnant. Your e-mails and texts expose your intimate and casual friends. Google knows what you’re thinking because it saves your private searches. Facebook can determine your sexual orientation without you ever mentioning it.

T

51

Natural Language Processing with Python

Steven Bird, Ewan Klein, Edward Loper

4.2

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range

52

Learning From Data: A Short Course

Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin

4.2

Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we be

53

AI Superpowers: China, Silicon Valley, and the New World Order

Kai-Fu Lee

4.2

Dr. Kai-Fu Lee—one of the world’s most respected experts on AI and China—reveals that China has suddenly caught up to the US at an astonishingly rapid and unexpected pace. In AI Superpowers, Kai-fu Lee argues powerfully that because of these unprecedented developments in AI, dramatic changes will be happening much sooner than many of us expected. Indeed, as the US-Sino AI competition begins to hea

Recommended by Arianna Huffington, Satya Nadella, Yuval Noah Harari, and 13 others.

Arianna Huffington

Founder/The Huffington Post

Kai-Fu Lee's experience as an AI pioneer, top investor, and cancer survivor has led to this brilliant book about global technology. AI Superpowers gives us a guide to a future that celebrates all the benefits that AI will bring, while cultivating what is unique about our humanity. It’s one of those books you read and think, ‘Why are people reading any other book right now when this is so clearly the one they need to be reading? [source]

Satya Nadella

CEO/Microsoft

Kai-Fu Lee's smart analysis on human-AI coexistence is clear-eyed and a must-read. We must look deep within ourselves for the values and wisdom to guide AI's development. [source]

Yuval Noah Harari

Historian

A superb and very timely survey of the impact of AI on the geopolitical system, the job market and human society. [source]

54

Information Theory, Inference and Learning Algorithms

David J. C. MacKay

4.2

Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with appli

55

Visualize This: The FlowingData Guide to Design, Visualization, and Statistics

Nathan Yau

4.2

Practical data design tips from a data visualization expert of the modern age Data doesn't decrease; it is ever-increasing and can be overwhelming to organize in a way that makes sense to its intended audience. Wouldn't it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks to the creative genius of

Recommended by Roger D. Peng.

Roger D. Peng

This book is about how best to present data to other people, what are the tools that you can use, and the types of visualizations that you can make. [source]

56

The Functional Art: An Introduction to Information Graphics and Visualization

Alberto Cairo

4.1

Unlike any time before in our lives, we have access to vast amounts of free information. With the right tools, we can start to make sense of all this data to see patterns and trends that would otherwise be invisible to us. By transforming numbers into graphical shapes, we allow readers to understand the stories those numbers hide. In this practical introduction to understanding and using informati

57

Superintelligence: Paths, Dangers, Strategies

Nick Bostrom

4.1

Superintelligence asks the questions: what happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us? Nick Bostrom lays the foundation for understanding the future of humanity and intelligent life.

The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant posi

Recommended by Elon Musk, Maria Ramos, and Will MacAskill.

Elon Musk

Founder/SpaceX

Worth reading Superintelligence by Bostrom. We need to be super careful with AI. Potentially more dangerous than nukes. [source]

Maria Ramos

CEO/Barclays Africa

Ramos will take the summer to examine some of the questions weighing more heavily on humankind as we contemplate our collective future: what happens when we can write our own genetic codes, and what happens when we create technology that is meaningfully more intelligent than us. The Gene: An Intimate History—Siddhartha Mukherjee Superintelligence: Paths, Dangers, Strategies—Nick Bostrom The Ministry of Utmost Happiness—Arundhati Roy [source]

Will MacAskill

I picked this book because the possibility of us developing human-level artificial intelligence, and from there superintelligence—an artificial agent that is considerably more intelligent than we are—is at least a contender for the most important issue in the next two centuries. Bostrom’s book has been very influential in effective altruism, lots of people work on artificial intelligence in order to ensure that it is developed safely. I don’t agree with the entire contents of the book, but there are many compelling arguments in it. It would be extremely overconfident to dismiss this book as too speculative. In fact, I think there should be a lot more work that tries to understand the biggest challenges for the next two hundred years, and what we could do to try to overcome them. [source]

58

The Design of Everyday Things

Don Norman

4.1

Even the smartest among us can feel inept as we fail to figure out which light switch or oven burner to turn on, or whether to push, pull, or slide a door. The fault, argues this ingenious—even liberating—book, lies not in ourselves, but in product design that ignores the needs of users and the principles of cognitive psychology. The problems range from ambiguous and hidden controls to arbitrary r

Recommended by Andrew Chen, Marissa Mayer, Nir Eyal, and 23 others.

Grey Baker

I mainly read to decompress and change my state of mind, so it’s hard to point to an insight I read that helped me. Reading fiction has pulled me out of a bad mood more times than I can count, though, and always reenergises me to attack problems that had stumped me again. That said, I read and loved Norman Norman’s “The Design of Everyday Things”, and it’s helped me think through design problems ever since. [source]

Julia Enthoven

For people interested in designing or building software products: The Design of Everyday Things by Don Norman and The Lean Product Playbook by Dan Olsen. Both books have informed my product sense and helped me make decisions about great UX. [source]

Kaci Lambe

These three books are about how people actually use design in their lives. They helped me understand this very basic idea: There are no dumb users, only bad designers. Take the time to create based on how your design will be interacted with. Test it. Iterate. That's how you become a good designer. [source]

59

Artificial Intelligence: A Modern Approach

Stuart Russell and Peter Norvig

4.1

For one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Adde

60

Now You See It: Simple Visualization Techniques for Quantitative Analysis

Stephen Few

4.1

61

Antifragile: Things That Gain from Disorder

Nassim Nicholas Taleb

4.1

From the bestselling author of The Black Swan and one of the foremost philosophers of our time, Nassim Nicholas Taleb, a book on how some systems actually benefit from disorder.

In The Black Swan Taleb outlined a problem; in Antifragile he offers a definitive solution: how to gain from disorder and chaos while being protected from fragilities and adverse events. For what he calls the "antifragile"

Recommended by Andrew Chen, Ev Williams, Marc Andreessen, and 20 others.

Founder/StockPickr

You ask about success. To be successful you have to avoid being “fragile” – the idea that if something hurts you, you let collapse completely. You also have to avoid simply being resilient. Bouncing back is not enough. Antifragile is when something tries to hurt you and you come back stronger. That is real life business. That is real life success. Nassim focuses on the economy. But when I read the book I kept asking myself, “how can I apply this to the areas of my life where I feel most fragile?” [source]

Marvin Liao

Partner/500 Startups

My list would be (besides the ones I mentioned in answer to the previous question) both business & Fiction/Sci-Fi and ones I personally found helpful to myself. The business books explain just exactly how business, work & investing are in reality & how to think properly & differentiate yourself. On the non-business side, a mix of History & classic fiction to understand people, philosophy to make sense of life and Science fiction to picture what the future could be like (not always utopian). [source]

Vlad Tenev

The general concept is applicable to many fields beyond biology, for instance finance, economics and monetary policy. [source]

62

Statistics Done Wrong: The Woefully Complete Guide

Alex Reinhart

4.1

Everyone knows that abuse of statistics is rampant in popular media. Politicians and marketers present shoddy evidence for dubious claims all the time. But smart people make mistakes too, and when it comes to statistics, plenty of otherwise great scientists--yes, even those published in peer-reviewed journals--are doing statistics wrong.

"Statistics Done Wrong" comes to the rescue with cautionary t

63

Don't Make Me Think, Revisited: A Common Sense Approach to Web Usability

Steve Krug

4.1

Since Don’t Make Me Think was first published in 2000, over 400,000 Web designers and developers have relied on Steve Krug’s guide to help them understand the principles of intuitive navigation and information design.

In this 3rd edition, Steve returns with fresh perspective to reexamine the principles that made Don’t Make Me Think a classic-–with updated examples and a new chapter on mobile usabil

Recommended by Brian Armstrong, Jeff Atwood, Ken Norton, and 13 others.

Anoop Anthony

Krug was teaching usability two decades ago when UX wasn't a buzzword, and the WWW was still in its nascent stages. (The first edition of this book was published in 2000!) I cut my teeth on Don't make me think back when I first started out in the industry. It is now in its third edition and still relevant — it offers compelling insights into building great user experiences into websites and applications. [source]

Auston Bunsen

I’m actually a self-taught programmer, so these books have really helped me with practical skills that I could put to use & yield results. The return on investment for these kinds of books is off the charts for me! [source]

Chris Goward

Here are some of the books that have been very impactful for me, or taught me a new way of thinking: … Don't Make Me Think. [source]

64

Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites

Matthew A. Russell

4.1

Want to tap the tremendous amount of valuable social data in Facebook, Twitter, LinkedIn, and Google+? This refreshed edition helps you discover who’s making connections with social media, what they’re talking about, and where they’re located. You’ll learn how to combine social web data, analysis techniques, and visualization to find what you’ve been looking for in the social haystack—as well as u

65

Practical Data Science with R

Nina Zumel, John Mount, Jim Porzak

4.1

Simply put, data science is the discipline of extracting meaning from data. While it can involve deep knowledge of statistics, mathematics, machine learning, and computer science, for most non-academics, data science looks like applying analysis techniques to answer key business questions.

Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo

66

Doing Bayesian Data Analysis: A Tutorial Introduction with R

John K. Kruschke

4.1

There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained i

67

R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics

Paul Teetor

4.1

With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, gr

68

Introduction To Algorithms

Thomas H. Cormen, Charles E. Leiserson, et al.

4.1

69

ggplot2: Elegant Graphics for Data Analysis

Hadley Wickham

4.1

1. 1 Welcome to ggplot2 ggplot2 is an R package for producing statistical, or data, graphics, but it is unlike most other graphics packages because it has a deep underlying grammar. This grammar, based on the Grammar of Graphics (Wilkinson, 2005), is composed of a set of independent components that can be composed in many di?erent ways. This makesggplot2 very powerful, because you are not limited

70

Numsense! Data Science for the Layman: No Math Added

Annalyn Ng and Kenneth Soo

4.1

---------------
Reference text for data science in top universities like Stanford and Cambridge. Sold in over 85 countries and translated into more than 5 languages.
---------------

Want to get started on data science?
Our promise: no math added.

This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that ex

71

R Graphics Cookbook: Practical Recipes for Visualizing Data

Winston Chan

4.1

This practical guide provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R's graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.

Most of the recipes use the ggplot2 package, a powerful and flexible way to ma

72

Think Stats

Allen B. Downey

4.1

If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and ge

73

Bayesian Data Analysis

Andrew Gelman, John B. Carlin, Hal S. Stern, Donald B. Rubin

4.1

Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, base

74

How Not to Be Wrong: The Power of Mathematical Thinking

Jordan Ellenberg

4.1

The Freakonomics of math—a math-world superstar unveils the hidden beauty and logic of the world and puts its power in our hands

The math we learn in school can seem like a dull set of rules, laid down by the ancients and not to be questioned. In How Not to Be Wrong, Jordan Ellenberg shows us how terribly limiting this view is: Math isn’t confined to abstract incidents that never occur in real life

Recommended by Bill Gates, Auston Bunsen, and Nick Ganju.

CEO/Microsoft

The writing is funny, smooth, and accessible -- not what you might expect from a book about math. What Ellenberg has written is ultimately a love letter to math. If the stories he tells add up to a larger lesson, it’s that 'to do mathematics is to be, at once, touched by fire and bound by reason' -- and that there are ways in which we’re all doing math, all the time. [source]

Auston Bunsen

I’ve got a few, one book that really impacted me early on as someone coming from a middle-class family was “Rich dad, Poor dad”. Since then I’ve read many books but one that really stands out is “How not to be wrong” by Jordan Ellenberg which really reignited my appetite & appreciation for math. [source]

Nick Ganju

Written for an audience of people who have historically been intimidated by math … and introduces things in a very simple way, and then works up to more complex concepts. [source]

75

Mining of Massive Datasets

Anand Rajaraman, Jeffrey David Ullman

4.1

The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms auto

76

Life 3.0: Being Human in the Age of Artificial Intelligence

Max Tegmark

4.1

How will Artificial Intelligence affect crime, war, justice, jobs, society and our very sense of being human? The rise of AI has the potential to transform our future more than any other technology--and there's nobody better qualified or situated to explore that future than Max Tegmark, an MIT professor who's helped mainstream research on how to keep AI beneficial.

How can we grow our prosperity th

Recommended by Barack Obama, Bill Gates, Elon Musk, and 2 others.

Barack Obama

Former USA President

As 2018 draws to a close, I’m continuing a favorite tradition of mine and sharing my year-end lists. It gives me a moment to pause and reflect on the year through the books I found most thought-provoking, inspiring, or just plain loved. It also gives me a chance to highlight talented authors – some who are household names and others who you may not have heard of before. Here’s my best of 2018 list. [source]