30 Great Books on Data Science and Big Data

By CSDH Staff
December 2017

Technology may be rapidly becoming the future, but some of the best knowledge can still be found in books.

As data science and big data continue to solidify their places in the next wave of industrial revolution, more and more books are being written on the topics. Aimed at everyone from aspiring data scientists to do-it-yourself business managers, these books are often written by experts and contain myriad facts and tips for using the principles of data science in order to drive innovation, improve customer satisfaction, and increase revenue. Whether you’re an advanced data scientist or a beginner looking for the basics, the 30 books listed below are some of the best on the subject.

Analytics in a Big Data World: The Essential Guide to Data Science and its Applications

Bart Baesens


Data science expert Bart Baesens reveals the ways in which data science can be used as an advantage in running a business and identifying new opportunities. “Analytics in a Big Data World” is an accessible resource that readers can turn to again and again for information about using big data, analytics, and various applications for things like marketing, credit risk, fraud, customer relationship management, and much more.

Big Data: A Beginner’s Guide to Using Data Science for Business

Eliot P. Reznor


Subtitled “Transforming Information, Deep Learning, Boost Profits, Business Intelligence,” this book by Eliot P. Reznor is chock-full of real-world examples about how big data can positively impact real people and their businesses. “Big Data” explores practical uses for, and even explores the future of, big data and data science.

Big Data: A Very Short Introduction

Dawn E. Holmes


Sometimes all you need (or want!) are the basic facts. If that’s you, then look no further than “Big Data” in the A Very Short Introduction series. This newly released book offers readers a solid working knowledge of exactly what big data is (and what it isn’t), how it came to transform the worlds of business and technology, and how far experts expect it to advance next.

Big Data: Algorithms, Analytics, and Applications

Kuan-Ching Li, Hai Jiang, Laurence T. Yang, and Alfredo Cuzzocrea


The knowledge of leading experts is compiled into this book, which covers big data from the perspective of algorithms and other computational methods. It is organized into five different sections — big data management, big data processing, big data stream techniques and algorithms, big data privacy, and big data applications — each of which includes practical applications, models and techniques, and examples from the real world.

Big Data and Social Science: A Practical Guide to Methods and Tools

Ian Foster and Rayid Ghani


Drawing on the expertise of figures working in statistics, data science, computer science, and even the social sciences, authors Ian Foster and Rayid Ghani discuss the ways in which data science can be applied to real-world problems. “Big Data and Social Science,” which was written for both students and working professionals, provides its readers with the practical knowledge needed to combine various math and science disciplines in order to approach things like innovation.

Big Data: Does Size Matter?

Timandra Harkness


Those interested in the real-world application of data science will find this book by Timandra Harkness absolutely fascinating. Harkness presents a variety of stories and people related to current-day data science in order to ask such valid questions as “What are the unspoken assumptions underlying [data science and] its methods?” and “Are you a data point, or a human being?”

Big Data for Business: Your Comprehensive Guide to Understand Data Science, Data Analytics, and Data Mining to Boost More Growth and Improve Business

Victor Finch


Victor Finch’s “Big Data for Business” is a must-read for business owners unsure about what data science is or how it can help create customers and revenue. Through unique insight and detailed real-world examples, readers will learn about how to overcome common challenges, how to use descriptive and predictive analytics, the most common tools used by data scientists, various algorithms, and so much more.

Big Data: How the Information Revolution is Transforming Our Lives

Brian Clegg


Brian Clegg’s “Big Data: How the Information Revolution is Transforming Our Lives” looks at various markets dominating our lives and describes the impact big data has had. Readers will gain fascinating insight into such questions as, “Why do airlines overbook?”, “Was the Brexit vote successful big data politics or the end of democracy?”, “How does big data enable Netflix to forecast a hit?”, and more.

Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results

Bernard Marr


Big Data in Practice, written by bestselling author Bernard Marr, illustrates for readers the many ways in which big data is used by major companies. He profiles a variety of companies ranging from Amazon and Target, to John Deere and Apple, and details how each company uses big data to encourage innovation, improve customer service, and ultimately improve their product and revenue.

Big Data MBA: Driving Business Strategies with Data Science

Bill Schmarzo


“Big Data MBA” may be the closest business owners can get to one-on-one advice without hiring the personalized services of a consultant. Bill Schmarzo’s book, which is written around a practical and proven framework, offers the reader hands-on exercises and tips for using big data to transform their business. The list of topics covered is long and varied, and includes where and how to leverage big data, structuring an organization to drive analytic insights, and how to “think like a data scientist.”

Big Data Science & Analytics: A Hands-On Approach

Arshdeep Bahga and Vijay Madisetti


“Big Data Science & Analytics” is the name of both this book and the [upcoming] “fourth industrial revolution” around which it is focused. This comprehensive book covers a wide range of big data analytics, from modern analytic methods to an overview of specific software tools, and more. Each of the book’s three parts are written in clear language, with helpful diagrams and code examples included throughout.

Big Data, Small Devices: Investigating the Natural World Using Real-Time Data

Donna Governor and Michael Bowen


Written specifically for teachers of Earth and environmental sciences, this fun book will help turn students into amateur data scientists and mobile phones and tablets into tools. The book is full of tips and ‘how to’s for using existing websites and apps to track and detect patterns in the atmosphere, biosphere, geosphere, and the seasons.

Data Analytics: A Practical Guide to Data Analytics for Business, Beginner to Expert

James Fahl


James Fahl’s “Data Analytics” is a step-by-step guide for those who want to understand analytics in order to increase their revenue. Beginners will appreciate its solid summary of what data analytics is and why it is so important, while more advanced users will benefit from the easy-to-follow tips for understanding the various models, collecting data, and avoiding common mistakes.

Data Analytics: Master the Techniques for Data Science, Big Data, and Data Analytics

Robert Keane


Robert Keane’s “Data Analytics” provides readers with a thorough understanding of data, thereby giving business owner’s the edge they need to succeed in the marketplace. The book walks readers through how to get started with data analysis, the best ways to use predictive analysis, and various techniques for using machine learning and data science in their life.

Data Analytics: Practical Guide to Leveraging the Power of Algorithms, Data Science, Data Mining, Statistics, Big Data, and Predictive Analysis to Improve Business, Work, and Life

Arthur Zhang


“Data Analytics” is perfect for the reader who is new to the topic, as it provides just enough detail for the read to gain a solid working knowledge of how data science and analytics can improve both business and life. By the last page, readers will have gained key insights into such topics as data gathering and scrubbing, big data for small businesses, descriptive modeling, machine learning techniques, hyper targeting, Google trends, and so much more.

Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing, and Presenting Data

EMC Education Services


EMC Education’s “Data Science and Big Data Analytics” — one of Amazon’s highest ranked books on the topic — focuses on the ideas, principles, and applications that can be applied to any business or industry type. This book walks its readers through a number of topics, ultimately guiding them towards becoming a contributor to data science by teaching them such concepts as storytelling with data, analyzing big data, and much more.

Data Science: Create Teams That Ask the Right Questions and Deliver Real Value

Doug Rose


Business managers who aspire to in-house data science, as opposed to hiring outside talent, will appreciate this book. Author Doug Rose guides his readers through the building of an effective data science team. Topics include finding existing talent within an organization, which roles to fill and their respective responsibilities, and important data science concepts, to name but a few.

Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking

Foster Provost and Tom Fawcett


Written by two data science experts, “Data Science for Business” is commonly used as a textbook. Readers will learn the basic principles of data science, be walked through “data-analytic thinking,” and come to understand today’s most commonly used data-mining techniques.

Data Smart: Using Data Science to Transform Information into Insight

John W. Foreman


Business owners don’t need to hire a data scientist to better understand their customer base and increase their revenue — they can do it themselves! So says John W. Foreman, author of the Amazon bestseller “Data Smart.” This book walks readers through the new world big data has created, showing them how they can use the same strategies as big companies to benefit.

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

Seth Stephens-Davidowitz


Everybody Lies, a bestseller on Amazon, uses big data to ask interesting questions about human nature. Topics investigated include whether violent movies actually affect the crime rate, how to beat the stock market (if you can), how often humans lie about their sex lives, and voting trends.

Getting Started with Data Science: Making Sense of Data with Analytics

Murtaza Haider


Written in the same style that made books like “Freakonomics” and “Outliers” so popular, “Getting Started with Data Science” uses fascinating narratives in order to show readers exactly what it takes to make big data and data science work for them. A wide range of topics are covered in the form of interesting questions, including “Are religious individuals more or less likely to have extramarital affairs?”, “Does the presence of children influence a family’s spending on alcohol?”, and “What determines housing prices more: lot size or the number of bedrooms?”

The Intelligence Enterprise in the Era of Big Data

Venkat Srinivasan


In “The Intelligence Enterprise in the Era of Big Data,” Venkat Srinivasan focuses on the narrowing gap between business and technology. Through real-world case studies and detailed examples, data scientists, business executives, and technology professionals will gain a solid understanding of the ways in which data mining and big data have become synonymous with business intelligence.

Introducing Data Science: Big Data, Machine Learning, and More, Using Python Tools

Davy Cielen, Arno Meysman, and Mohamed Ali


This bestselling book written by Python experts will guide readers through the basic principles of data science. The book covers the Python language and its common libraries, provides a helpful introduction to machine learning, and shows readers how to handle large data and write data science algorithms.

Numsense! Data Science for the Layman: No Math Added

Annalyn Ng and Kenneth Soo


Numsense! is the ideal book about big data and data science, and was even used by Stanford in its spring 2017 Big Data course. The book, written in layman’s terms, is perfect for the reader new to data science. It covers such topics as A/B testing, clustering, regression analytics, neural networks, social network analysis, anomaly detection, and so much more.

The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement

Andrew Guthrie Ferguson


While most of the books on our list are books of practical advice for data scientists, this one looks at the topic from a different perspective: law enforcement. “The Rise of Big Data Policing” discusses the ways in which data science is changing the way police do their jobs, and why citizens should be aware of these changes.

Storytelling with Data: A Data Visualization Guide for Business Professionals

Cole Nussbaumer Knaflic


This Amazon bestseller is a must-read for any data scientist. The book walks readers through the steps of visualizing data and using it to communicate effectively — in other words, how to tell a story using data. Specifically, readers will finish the book having learned such necessary skills as determining appropriate graphs for various situations, recognizing what’s important data and what is just clutter, and directing an audience’s attention to the most important parts of data, to name but a few things.

Too Big to Ignore: The Business Case for Big Data

Phil Simon


Insurance companies tracking real-time customer driving patterns, citizens reporting potholes on their smartphones — such technological victories are possible because of big data and data science. In “Too Big to Ignore,” author Phil Simon looks at dozens of real-world examples to summarize the evolution and future of big data. The book is a must-read for those in the early stages of figuring out exactly what data science is and how they can put it to use.

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

Cathy O’Neil


Author Cathy O’Neil, a former Wall Street tycoon, wrote this bestselling book about the ways in which algorithms are changing the world — sometimes for the worse. She describes the ways in which big data has come to determine whether or not a student from a poor zip code gets a college loan, how employers sort resumes, how insurance companies monitor our health, and other situations from the dark side of data science.

What Every Manager Should Know About Big Data and Data Science

Lars Nielsen and Noreen Burlingame


“What Every Manager Should Know About Big Data and Data Science” focuses on the real-time Business Intelligence (BI) aspect of data science. Aimed at business managers in particular, it details the need-to-know basics of data science, how it can enhance BI, and the various processes by which it functions.

What To Do When Machines Do Everything: How to Get Ahead in a World of AI, Algorithms, Bots, and Big Data

Malcolm Frank and Paul Roehrig


This thought-provoking book looks ahead at the future of technology. Referring to itself as a guidebook, it offers actionable steps that today’s businesses can take to survive in a world in which artificial intelligence and big data drive everything from cars to hospitals.