Best Data Analysis Books of 2023

Our top pick for best data analysis books is Foster Provost and Tom Fawcett’s Data Science for Business, which teaches you the ins and outs of the “data-analytic thinking” needed to extract useful insights from data and apply them to your business. The book walks you through the steps for using data analysis to guide business decisions, whether you’re a seasoned professional or a new candidate on the job market.

Big Data is the first book on this highly timely subject: it teaches you what big data is, how and by whom big data is used, and what that means for you. The book offers a practical and optimistic look at the big data revolution, which, according to the authors, is only just beginning.

Barry Devlin’s Business unIntelligence is a manual for the contemporary world of business, a world in which you must make decisions at the speed of thought using a combination of logic and intuition. The book contains many new models that business and IT professionals can use to support company growth into the future.

Written by award-winning author and experienced computer scientist Melanie Mitchell, Artificial Intelligence: A Guide for Thinking Humans separates fact from fiction and lays out the current landscape of AI. The book also addresses the promise and potential dangers of AI’s continued growth, and anecdotes, humor, and personal observations are peppered throughout the text.

Business Data Science is a practical guide to data science that weaves together insights from business, economics, and statistical analyses to help you understand your customers, frame decisions, and increase value. The book can serve as an introduction for beginners and a reference text for those well-versed in business data science.

Creating Value with Social Media Analytics is all about understanding social media analytics and harnessing their power to achieve your business goals. The book is full of concepts, strategies, tools, tutorials, and case studies that brands and businesses can use to identify and better market to their customers. The book is accessible for students and people without a tech background.

As the title suggests, Data Analytics Made Accessible is a reader-friendly guide to data mining for beginners and for those who don’t want to be bogged down by overly technical language. The author structures the chapters around a typical one-semester university course, but you can also use the book for self-study. Real-world case studies anchor the concepts and make them easier to grasp.

Python for Data Analysis provides you with clear, comprehensive instructions for manipulating, processing, cleaning, and crunching data sets using Python. The book is best suited to data scientists who want to expand their repertoire to Python; readers should have a working knowledge of data analysis before diving in.

Phil Simon’s Too Big to Ignore explains why big data is such a big deal and how it affects nearly every aspect of our present-day lives. The book provides accessible, jargon-free insights for anyone who wants to understand and harness the power of big data, especially business executives, entrepreneurs, and industry leaders.

Written by a veteran business analyst, A Practitioner’s Guide to Business Analytics is a how-to guide for leaders who want to evolve their businesses from a traditional corporate structure to an analytics-driven environment. The book is full of real-world insights and practical lessons learned from business analytics successes and failures.

In Data Smart, author John Foreman walks readers step by step through the process of transforming data into actionable insights, using the accessible form of a spreadsheet as an example. The book is accessible to nontech people and gradually builds to more difficult knowledge and skills, allowing readers to build a solid foundation.

Written by one of the nation’s most well-respected physicians, Deep Medicine explores AI’s potential to revolutionize medicine and health care for the better. The book lays out the myriad ways AI can foreseeably empower doctors and drastically improve patient care and is a worthwhile read for health care professionals or anyone with a vested interest in medical care.

Developing Analytic Talent is a resource for anyone looking to land a job as a data scientist or seeking to hire a data scientist. The book not only walks you through the key concepts and skills of data science but also includes insights about what employers are looking for as well as sample resumes, interview questions, job descriptions, and salary ranges.

Alistair Croll and Benjamin Yoskovitz’s Lean Analytics helps startups measure and analyze as they grow so they can successfully carve out a niche for their business and meet the needs of an untapped market. This book lays out practical steps that can take your startup from the idea phase to product/market fit and more.

Written by the bestselling author of Naked Economics, the follow-up text Naked Statistics strips away the jargon, tedium, and “dread” from data analysis and offers an accessible foray into the increasingly alluring world of statistics. Written in a witty, conversational tone and packed with interesting case studies, the book will appeal to anyone from students and hobbyists to professionals.

Numsense! is a gentle introduction to data science and its algorithms, written in layman’s terms with no required math. The book can be used as a reference text at top universities and is sold in over 85 countries. Each algorithm has its own chapter, complete with case studies, showing you how it works in the real world.

Unlike most introductory statistics books, Practical Statistics for Data Scientists is written specifically for data scientists, curating statistical concepts and methods that data scientists need to understand. The book is best suited to readers who are familiar with the R or Python programming languages and who have a basic understanding of statistics.

In Rebooting AI, longtime AI researchers Gary Marcus and Ernest Davis lay out what we will need to do to bridge the gap between current AI and the vast, complex powers of the human mind. They offer a clear, commonsense assessment of the present-day state of the field and an optimistic view of what the next generation of AI can do for the future of humanity.

Storytelling with Data provides you with a foundational understanding of data visualization and explains how to use data to communicate effectively. The book teaches you how to apply narrative fundamentals to storytelling with data so you can craft a message that resonates with your audience. You also learn how to incorporate the principles of design into your data visualization and storytelling.

Written by world-renowned statistician David Spiegelhalter, The Art of Statistics is a comprehensive guide to statistical thinking that teaches readers how to evaluate and draw insights from data. The book illustrates how to reason and problem-solve like a statistician, even if you have no background in statistics, economics, data analysis, or related fields.