The best statistics books

10 authors have picked their favorite books about statistics and why they recommend each book.

Soon, you will be able to filter by genre, age group, and more. Sign up here to follow our story as we build a better way to explore books.

Shepherd is reader supported. When you buy through links on our website, we may earn an affiliate commission (learn more).

The Art of Statistics

By David Spiegelhalter,

Book cover of The Art of Statistics: How to Learn from Data

What if you are faced with a problem for which a standard approach doesn’t yet exist? In such a case, you will need to be able to figure out the approach from the first principles. This book will help you learn how to derive insights starting from raw data.


Who am I?

I started my career as a research scientist building machine learning algorithms for weather forecasting. Twenty years later, I found myself at a precision agriculture startup creating models that provided guidance to farmers on when to plant, what to plant, etc. So, I am part of the movement from academia to industry. Now, at Google Cloud, my team builds cross-industry solutions and I see firsthand what our customers need in their data science teams. This set of books is what I suggest when a CTO asks how to upskill their workforce, or when a graduate student asks me how to break into the industry.


I wrote...

Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time Data Pipelines: From Ingest to Machine Learning

By Valliappa Lakshmanan,

Book cover of Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time Data Pipelines: From Ingest to Machine Learning

What is my book about?

This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on Google Cloud Platform (GCP).

Through the course of this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science.

Counting

By Deborah Stone,

Book cover of Counting: How We Use Numbers to Decide What Matters

I had never really given much thought to counting until I read this book, but in the very first chapter, Stone made me rethink everything I thought I knew about “one fish, two fish, red fish, blue fish.” She shows that every time we count, we’re making cultural assumptions. For example, what counts as a fish? And what makes the color of the fish more relevant than other features? Counting reveals that while these choices may seem intuitive, basic, and meaningless, they have very real impacts on people’s lives. Especially when we use numbers to measure things like merit, poverty, race, and productivity, those fundamental assumptions matter more than we care to admit.  


Who am I?

I’m a historian who’s spent far too much time thinking about how the color magenta contributed to climate change and why eighteenth-century humanitarians were obsessed with tobacco enemas. My favorite historical topics—like sensation, color, and truth—don’t initially seem historical, but that’s exactly why they need to be explored. I’ve learned that the things that seem like second nature are where our deepest cultural assumptions and unconscious biases hide. In addition to writing nonfiction, I’ve been lucky enough to grow up on a ranch, live in Paris, work as an interior design writer, teach high school and college, and help stray dogs get adopted.


I wrote...

The Sensational Past: How the Enlightenment Changed the Way We Use Our Senses

By Carolyn Purnell,

Book cover of The Sensational Past: How the Enlightenment Changed the Way We Use Our Senses

What is my book about?

Blindfolding children from birth? Playing a piano made of live cats? Using tobacco to cure drowning? Wearing "flea"-colored clothes? These actions may seem odd to us, but in the eighteenth century, they made perfect sense.

As often as we use our senses, we rarely stop to think about their place in history. But perception is not dependent on the body alone. Carolyn Purnell persuasively shows that, while our bodies may not change dramatically, the way we think about the senses and put them to use has been rather different over the ages. Journeying through the past three hundred years, Purnell explores how people used their senses in ways that might shock us now. And perhaps more surprisingly, she shows how many of our own ways of life are a legacy of this earlier time.

Statistics and Data Analysis for Financial Engineering

By David Ruppert, David S. Matteson,

Book cover of Statistics and Data Analysis for Financial Engineering: With R Examples

I have used this book to teach my Financial Risk Analytics course at Northwestern University for many years. As a textbook, it is surprisingly easy to read, and the abundant exercises are great. This would be a foundational text to read after you have read my own books. It puts you on solid ground to understand all the financial babble that you may read elsewhere. It includes extensive coverage of most basic topics important to a serious quantitative trader, while not being overly mathematical. Easily understandable if you have basic programming and math background from first year of university.

Everything is practical in this book, which isn’t what you would expect from a textbook! There is no math for math’s sake. I have used the techniques discussed in this book for real trading, and for creating features at my machine learning SaaS predictnow.ai. Examples: What’s the difference between net…

Who am I?

A noted quantitative hedge fund manager and quant finance author, Ernie is the founder of QTS Capital Management and Predictnow.ai. Previously he has applied his expertise in machine learning at IBM T.J. Watson Research Center’s Human Language Technologies group, at Morgan Stanley’s Data Mining and Artificial Intelligence Group, and at Credit Suisse’s Horizon Trading Group. Ernie was quoted by Bloomberg, the Wall Street Journal, New York Times, Forbes, and the CIO magazine, and interviewed on CNBC’s Closing Bell program. He is an adjunct faculty at Northwestern University’s Master’s in Data Science program and supervises student theses there. Ernie holds a Ph.D. in theoretical physics from Cornell University.


I wrote...

Quantitative Trading: How to Build Your Own Algorithmic Trading Business

By Ernest P. Chan,

Book cover of Quantitative Trading: How to Build Your Own Algorithmic Trading Business

What is my book about?

Can a robot take over your trading while you sip Tequila at the poolside? This book will show you how. You only need rudimentary programming skills, a tiny dose of math, and a healthy dose of grit.

In the newly revised Second Edition of Quantitative Trading: How to Build Your Own Algorithmic Trading Business, I show you how to apply both time-tested and novel quantitative trading strategies. You’ll discover new case studies and updated information on the application of cutting-edge machine learning investment techniques, as well as updated back tests on a variety of trading strategies, which included Matlab, Python, and R code examples. You will also find a guide to selecting the best traders and advisors to manage your money.

Modern Mathematical Statistics with Applications

By Jay L. DeVore, Kenneth N. Berk,

Book cover of Modern Mathematical Statistics with Applications

One of my favorite professors, Gretchen Martinet, used this to teach a course called “Mathematical Statistics” when I was at the University of Virginia. It is an extremely profound course full of dense but fundamental mathematical proofs in classical statistics. 

You will learn why the formula for the normal distribution is the way it is, why the sum of squares appears everywhere in statistics, and how to fit a linear regression by hand. In the same way calculus elevates our understanding of rates of changes, the book elevates your understanding of samples, averages, and distributions. Quant trading requires an intuitive sense of how data, models, and aggregates work, making this content essential for your success.


Who am I?

I am a financial data scientist. I think it is important that data scientists are highly specialized if they want to be effective in their careers. I run a business called Conlan Scientific out of Charlotte, NC where me and my team of financial data scientists tackle complicated machine learning problems for our clients. Quant trading is a gladiator’s arena of financial data science. Anyone can try it, but few succeed at it. I am sharing my top five list of math books that are essential to success in this field. I hope you enjoy.


I wrote...

Algorithmic Trading with Python: Quantitative Methods and Strategy Development

By Chris Conlan,

Book cover of Algorithmic Trading with Python: Quantitative Methods and Strategy Development

What is my book about?

Algorithmic Trading with Python discusses modern quant trading methods in Python with a heavy focus on pandas, numpy, and scikit-learn. After establishing an understanding of technical indicators and performance metrics, readers will walk through the process of developing a trading simulator, strategy optimizer, and financial machine learning pipeline. 

This book maintains a high standard of reproducibility. All code and data are self-contained in a GitHub repo. The data includes hyper-realistic simulated price data and alternative data based on real securities. 

How to Lie with Statistics

By Darrell Huff, Irving Geis (illustrator),

Book cover of How to Lie with Statistics

How to Lie with Statistics is a book that I highly recommend to anybody just starting out in data science. While we would like to believe that data science is a science many times it’s not, it’s storytelling. This storytelling with data can quickly get us into trouble. Whether it’s shortening y-axis or presenting data in a way that makes things look better than they are.

Personally I have found this book to be invaluable especially when working with business leaders as to why I won’t do certain things to my models and presentations.


Who am I?

My personal passion behind ethical AI started early in my life. I was raised by someone who had a personality disorder, and grew up being gaslit and manipulated. It was hard for me personally to understand what was reality and what was made up. Being a nerdy kid, I spent most of my time studying computers and math to escape it all. And while I have made my own life writing books on machine learning, and programming for a living, I also care deeply about how what I do affects others. Being thoughtful is deep within me, and I sit with a Zen group and volunteer with the Mankind Project.


I wrote...

Thoughtful Machine Learning with Python: A Test-Driven Approach

By Matthew Kirk,

Book cover of Thoughtful Machine Learning with Python: A Test-Driven Approach

What is my book about?

Thoughtful Machine Learning with Python is a book that details how to build models using care with test-driven development (TDD). TDD is one method for testing your assumptions and a way to step towards the ethical use of models.

R in Action

By Robert I. Kabacoff,

Book cover of R in Action: Data Analysis and Graphics with R

This provides a superb balance between technical aspects of R coding and the statistical methods that motivate its use. It's rare to find a book on topics like this that are written with Kabacoff's easygoing yet precise style, which makes it ideal for beginners. From my own experience, it is obvious the author has spent many years teaching this type of content, knowing where things deserve extra explanation up front and where other more technical details can be relegated to more advanced texts.


Who am I?

I’m an applied statistician and academic researcher/lecturer at New Zealand’s oldest university – the University of Otago. R facilitates everything I do – research, academic publication, and teaching. It’s the latter part of my job that motivated my own book on R. From first-year statistics students who have never seen R to my own Ph.D. students using R to implement novel and highly complex statistical methods and models, my experience is that all ultimately love the ease with which the R language permits exploration, visualisation, analysis, and inference of one’s data. The ever-growing need in today’s society for skilled statisticians and data scientists means there's never been a better time to learn this essential language.


I wrote...

The Book of R: A First Course in Programming and Statistics

By Tilman M. Davies,

Book cover of The Book of R: A First Course in Programming and Statistics

What is my book about?

The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis.

You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modelling. You’ll even learn how to create impressive data visualisations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualisations using the rgl package.

A First Course in Statistical Programming with R

By W. John Braun, Duncan J. Murdoch,

Book cover of A First Course in Statistical Programming with R

From well-known authorities in the R-sphere (including a former R Core Team member), this is a long-standing text whose first edition was one of the early books intended to teach R to beginners. It provides concise instructions and examples on how R is used as a programming language before focusing on 'number-crunching' statistical methods that are typically seen as computationally intensive. One of the notable features of this book is the statistical methods at hand are not just illustrated using 'black-box' code--the reader is provided with the necessary mathematical detail to understand what's going on behind the scenes for those that are so inclined.


Who am I?

I’m an applied statistician and academic researcher/lecturer at New Zealand’s oldest university – the University of Otago. R facilitates everything I do – research, academic publication, and teaching. It’s the latter part of my job that motivated my own book on R. From first-year statistics students who have never seen R to my own Ph.D. students using R to implement novel and highly complex statistical methods and models, my experience is that all ultimately love the ease with which the R language permits exploration, visualisation, analysis, and inference of one’s data. The ever-growing need in today’s society for skilled statisticians and data scientists means there's never been a better time to learn this essential language.


I wrote...

The Book of R: A First Course in Programming and Statistics

By Tilman M. Davies,

Book cover of The Book of R: A First Course in Programming and Statistics

What is my book about?

The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis.

You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modelling. You’ll even learn how to create impressive data visualisations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualisations using the rgl package.

Exploratory Data Analysis

By John Tukey,

Book cover of Exploratory Data Analysis

I learned Tukey’s name about as soon as I learned that data visualization existed as more than a menu in Excel and a personal obsession. Tukey coined the term “exploratory data analysis,” and so tapped into a passion for swimming around in all the interesting rows and columns. Tukey was working before computers were widespread, and so I got a view of how he saw data: working against the constraints of pencil and paper; keeping your hand moving as fast as possible. While the explorations we can do with gigabytes of memory and powerful rendering are very different, the goal of getting information into your head as fast as possible is unchanged.


Who am I?

In sixth grade, my teacher tried to teach the class how to read line charts – and something fell into place for me. Ever since then, I’ve tried to sort data into forms that we can use to make sense of it. As a researcher at Microsoft, I consulted with teams across the organization – from sales to legal; and from Excel to XBox – to help them understand their data. At Honeycomb, I design tools for software operations teams to diagnose their complex systems. These books each gave me an “ah-hah” moment that made me think differently about the craft of creating visualization. They now sit on my shelf in easy reach – I hope you find them fascinating too.


I wrote...

Making Data Visual: A Practical Guide to Using Visualization for Insight

By Danyel Fisher, Miriah Meyer,

Book cover of Making Data Visual: A Practical Guide to Using Visualization for Insight

What is my book about?

On a fairly regular basis, people come to me with a pile of data and say, “Hey, can you visualize this for me?” I’ve learned that the important part of that process isn’t the visualization – it’s refining the underlying question to be answered. Which parts of the data actually matter? Where is the insight hiding? My co-author and I wrote this book to capture a set of straightforward steps that helps refine your questions and map them to a visualization. If you’re trying to navigate the murky space between data and insight, this practical book shows you how to make sense of your data through high-level questions, well-defined data analysis tasks, and visualizations to clarify understanding and gain insights along the way.

The Tiger That Isn't

By Michael Blastland, Andrew Dilnot,

Book cover of The Tiger That Isn't: Seeing Through a World of Numbers

A book on statistics that is interesting? Yes, actually. And The Tiger that Isn’t is more than just interesting, it’s useful. Maths was never my strong point at school, but even someone who never got the hang of quadratic equations can learn to ask useful questions when faced with bamboozlingly large numbers and dodgy ‘averages’. 

This book offers a way to see through statistics that are used to conceal information as much as to reveal it. It’s worth reading just for the section on rice and random distribution. And the tiger in the title? It’s what happens when you think you see a pattern (in this case, stripes in the undergrowth), but there is no pattern at all. 


Who am I?

As a writer and historian, I’m all about rabbit holes. When something I’ve never heard about before catches my interest, I have to find out more—and sometimes I end up writing whole books on the subject! I have a head full of bizarre little nuggets of information, and I love reading books, like the ones here, that tell me something new and change my way of thinking. 


I wrote...

Why Everything You Know about Robin Hood Is Wrong: Featuring a pirate monk, a French maid, and a surprising number of morris dancers

By Karen C. Murdarasi,

Book cover of Why Everything You Know about Robin Hood Is Wrong: Featuring a pirate monk, a French maid, and a surprising number of morris dancers

What is my book about?

What if everything you ever knew about Robin Hood was wrong? He never met Maid Marian. He wasn’t a nobleman. He never went on Crusade. And he absolutely did not rob from the rich to give to the poor. 

Why Everything You Know about Robin Hood Is Wrong is an illuminating, entertaining, and really quite sarcastic trip through what we actually do know about one of England’s most famous heroes.

The Numbers Game

By Michael Blastland, Andrew Dilnot,

Book cover of The Numbers Game: The Commonsense Guide to Understanding Numbers in the News, in Politics, and in Life

I should declare an interest here: I present a BBC Radio show that Blastland and Dilnot created. This book was effectively my “how to” manual on the way into the studio that they had vacated. It’s a wise and varied guide to the power and the pitfalls of data, poetically written and full of subtle wisdoms.


Who am I?

Tim Harford is the author of nine books, including The Undercover Economist and The Data Detective, and the host of the Cautionary Tales podcast. He presents the BBC Radio programs More or Less, Fifty Things That Made The Modern Economy, and How To Vaccinate The World. Tim is a senior columnist for the Financial Times, a member of Nuffield College, Oxford, and the only journalist to have been made an honorary fellow of the Royal Statistical Society.


I wrote...

The Data Detective: Ten Easy Rules to Make Sense of Statistics

By Tim Harford,

Book cover of The Data Detective: Ten Easy Rules to Make Sense of Statistics

What is my book about?

Today we think statistics are the enemy, numbers used to mislead and confuse us. That's a mistake, Tim Harford says in The Data Detective. We shouldn't be suspicious of statistics--we need to understand what they mean and how they can improve our lives: they are, at heart, human behavior seen through the prism of numbers and are often "the only way of grasping much of what is going on around us." If we can toss aside our fears and learn to approach them clearly--understanding how our own preconceptions lead us astray--statistics can point to ways we can live better and work smarter.

Or, view all 17 books about statistics

New book lists related to statistics

All book lists related to statistics

Bookshelves related to statistics