17 Data Analytics Books You Should Read

There’s never been a better time to learn data analytics and enter the growing field. Industry leaders like IBM predict the number of open roles will increase from 364,000 to 2.7 million this year, and by 2028, job opportunities in data are expected to increase by 16 percent, according to the U.S. Bureau of Labor Statistics.

It’s clear the job landscape for data professionals is expanding, with a wide range of opportunities in a variety of industries. When preparing for a career in data analytics, the volume of information to master can be very overwhelming.

To help you get started, we have put together a list of 17 must-read data analytics books covering machine learning, big data, artificial intelligence, data science, Python, business intelligence, deep learning, forecasting and more. These books will help any reader understand the power of data and how to leverage it.

1) The Hundred-Page Machine Learning Book

By Andriy Burkov

Written by an expert in machine learning holding a Ph.D. in artificial intelligence and almost two decades of hands-on industry experience in computer science, this compact book is unique in many aspects. It can be read in only a few hours, but it offers a wealth of information without sacrificing quality information. Readers will enjoy an easy-to-understand piece that squeezes in a wide range of machine learning topics in a systematic way without shying away from the mathematics side of the industry.

2) Too Big to Ignore: The Business Case for Big Data

By Phil Simon

Whether you’re skeptical about or intrigued by business uses for big data, this is the go-to big data book in which the author examines how businesses and even local governments are using big data to their advantage. With several case studies and quotes from big data professionals across the globe, Too Big to Ignore: The Business Case for Big Data is a must-read for anyone considering entering the field. Readers will gain valuable insight on turning data into intelligence, and intelligence into something actionable.

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

By Viktor Mayer-Schönberger and Kenneth Cukier

This book is great for anyone seeking to understand why data analytics is important, and not just in a business sense. Data analytics can be applied across many industries, and this book offers information on how data can make an impact in your particular field, no matter what that might be. This data analytics book prepares readers for the reality that the big data revolution isn’t going anywhere anytime soon, and encourages them to embrace the industry changes to come.

4) Artificial Intelligence: A Guide for Thinking Humans

By Melanie Mitchell

In Artificial Intelligence, award-winning author and leading computer scientist Melanie Mitchell reveals AI’s turbulent history as well as the successes, hopes and emerging fears surrounding it. Throughout the book, Mitchell looks at the most urgent questions surrounding AI today: How do they work? Do we have to worry about them surpassing us? Mitchell also provides readers with a clear sense on the profound disconnect between the hype and actual achievements in AI, while interweaving stories about the science of AI and the people behind it.

5) Business unIntelligence: Insight and Innovation Beyond Analytics and Big Data

By Barry Devlin

Offering a look into the history of the field of business intelligence, this book provides a unique angle on big data and data analytics. Readers will get a look at the successes and issues in the field and discover things that aren’t always openly discussed within the industry, and will learn how many of the tried-and-true data practices have become outdated while gaining insight on how to stay competitive within the field. The book also explores the past, present and future of the field in order to refute many of the misconceptions related to modern data analytics and data gathering.

6) Creating Value With Social Media Analytics: Managing, Aligning, and Mining Social Media Text, Networks, Actions, Location, Apps, Hyperlinks, Multimedia, & Search Engines Data

By Gohar F. Khan

This book will teach you how to apply big data analytics to a social media strategy, helping drive value and engagement. As you move into a data career, this book will help you better understand the theories, concepts, strategies, techniques and resources required to retrieve business value from social media that can be used to improve customer loyalty, generate leads, increase traffic and ultimately make practical business decisions.

7) The Quick Python Book

By Naomi Ceder

The Quick Python Book offers a comprehensive guide to the Python language by Naomi Ceder, chair of the Python Software Foundation. The book beautifully balances details of the programming language with the insights and advice that readers can use to manage any task. Ceder provides extensive, relevant examples and learn-by-doing exercises to help readers understand each concept the first time through, making this a must-read for beginners preparing to enter the data analytics field.

8) Developing Analytic Talent: Becoming a Data Scientist

By Vincent Granville

After reading this book, you will have an understanding of how to develop detailed analytics that can help you meet business goals. The author explores the more intricate aspects of data science, the required skills and how to acquire them. The book also explores the skills that employers are looking for and how the growing reliance on big data has furthered the demand for data professionals. This in-depth book includes job interview questions, sample resumes, salary surveys and examples of job postings. Readers can also explore case studies that explain how data science is utilized on Wall Street, in botnet detection, in digital advertising and more.

9) A Practitioner’s Guide to Business Analytics: Using Data Analysis Tools to Improve Your Organization’s Decision Making and Strategy

By Randy Bartlett

This data analytics book first explores the steps involved in evaluating analytic prerequisites and abilities, then goes into how to develop a strategic plan to achieve business goals. The book offers the tools, information and strategies to help obtain valuable business analytics needed to make data-driven business decisions. Readers will discover how to distinguish business analytics, measure and demonstrate its value to an organization, and teach other data professionals how to leverage its capacity and discover new insights.

10) Data Analytics Made Accessible: 2020 Edition

By Anil K. Maheshwari, Ph.D.

If you’ve just started to learn about data, or if you’re not quite sure how it works, this book offers a wealth of information. Data Analytics Made Accessible breaks down data analysis into an easy-to-follow, digestible format. By exploring real-world examples (instead of complex hypothetical situations), readers at any skill level will be able to pick up this data analytics book and follow along to learn the basics. In fact, this resource is so well-received that several universities have included it in the required reading for many analytics courses.

11) Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

By Foster Provost and Tom Fawcett

Written by esteemed data science experts Foster Provost and Tom Fawcett, this book introduces the fundamental concepts of data science while introducing readers to data-analytic thinking that can be used for extracting useful knowledge and business value from data. After reading this book, you will discover how to think data-analytically and fully acknowledge how data science methods can support business decision-making.

12) Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again

By Eric Topol

In this book, leading physician Eric Topol reveals how artificial intelligence has the potential to empower physicians and revolutionize patient care, transforming everything doctors do, from notetaking and scans/imaging to diagnosis and treatment, significantly reducing the cost of medicine and mortality rates. Topal suggests that by freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard.

13) Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions

By Matt Taddy

The success of many companies rests on their ability to make and implement the right decisions quickly and effectively. Machine learning is a great way to understand customers, frame these decisions and drive value. This book takes readers through the steps of using machine learning to implement best-in-class business data science, and accelerate business decision-making. Whether you are just starting your career or you’re an experienced professional, you’ll find the information, insight and tools you need to thrive in today’s data-driven economy.

14) Python Tricks: A Buffet of Awesome Python Features

By Dan Bader

Mastering Python programming isn’t just about learning the theoretical aspects of the language. It’s also about understanding and adhering to the conventions and best practices used by its community. In this book, author Dan Bader helps readers discover proven methodologies and the power of Python with simple examples and a step-by-step narrative. Whether you’re new to the language or you’re looking to improve your skills, this book covers best practices and little-known tricks to round out your knowledge.

15) The Model Thinker: What You Need to Know to Make Data Work for You

By Scott E. Page

This book takes a deep dive into mathematical, statistical, and computational models – from linear regression to random walks and far beyond. Readers learn how to implement multiple models to organize data, resulting in better decision making, more accurate predictions and more consistent designs. The author also provides a toolkit for business people, learners, scientists, pollsters and bloggers to think strategically and better leverage data.

16) Rebooting AI: Building Artificial Intelligence We Can Trust

By Gary Marcus and Ernest Davis

In this book, two leaders in the field offer a compelling analysis of the current state of data science while revealing the steps we must take to achieve a truly robust artificial intelligence field. Taking inspiration from the human mind, the authors explain what we need in order to advance AI to the next level while creating an AI we can trust — in our homes, our cars and our doctors’ offices. If you’re interested in artificial intelligence, this book offers a current, accessible and balanced introduction to the field.

17) Python for Kids: A Playful Introduction to Programming

By Jason R. Briggs

The last book on our list is a playful introduction to the Python language. If you have a child or teenager, encouraging them to learn to code can open up countless opportunities, and this book can help them get started. Author Jason Briggs has been a programmer since the age of eight, when he first learned BASIC on a Radio Shack TRS-80. In his book, he instructs on the essentials of Python, and by the end, readers will have built a game and created drawings with Python’s graphics library, Turtle.

Summary

The data science books featured in this post will help anyone looking to gain insight into the growing field. Developing a solid understanding of data analytics and learning how to extract insights are vital components for a successful, long-term career regardless of the specific industry.

However, it’s important for potential and existing data professionals to stay up-to-date with the most relevant material in the industry. By reading these data analytics books, you will better understand how important data is to businesses today.

If you’d like to apply what you’ve learned from these books, explore our Berkeley Data Analytics Boot Camp designed to equip you with the skills to succeed in this booming industry.