30 Best Data Science Books

best data science books

By: CSDH Staff

The rise in big data has increased demand for data science careers. Majors in data analysis, data engineering, and data science have grown in number. Accredited colleges and universities offer some of the best data science programs. 

But an aspiring data scientist should know about the field they are getting into. For this reason, many data science books are available to read. With the industry growth, there are many big data books to choose. Some of the best books on data science are for beginners. But there are also books written for the experienced data scientist. 

Whether you’re an advanced data scientist or a beginner looking for the basics, these 30 books can help you in your career. Read on to find out more about the best data science books.

See Also: 50 Highest Paying Computer Science Careers

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

Bart Baesens

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

Data science expert Bart Baesens reveals the ways in which we use data science to run a business and identify new opportunities. The book is an accessible data science textbook that readers can turn to again and again for information about:

  • Credit risk
  • Customer relationship management
  • Data analysis
  • How to use data 
  • Learning data science
  • Marketing

The book examines applications in data engineering and gives readers an insight into the data science field. Some consider this one of the best data science books, as it reads like a data science handbook. 

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

Eliot P. Reznor

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

This big data book by Eliot P. Reznor is a good book for those learning data science. It is chock-full of real-world big data examples. It informs readers how to use data science to impact society, people, and business. It explores practical uses, shying away from data science concept theories. The book covers topics such as:

  • Data visualization
  • Deep learning
  • History of the data science process
  • How to inspire others to learn data science tools
  • Practical examples of data science strategy

Because of the practical application this book offers, it is one of the best books for data analytics beginners.

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3. Big Data: A Very Short Introduction

Dawn E. Holmes

Big Data: A Very Short Introduction

Sometimes all you need (or want!) are the basic facts. If this sounds like you, then look no further than the “A Very Short Introduction” series on learning data science by Dawn E. Holmes. This book offers readers a solid working knowledge of what big data is (and what it isn’t). The book also covers how data science came to transform the worlds of business and technology.

This is a book for beginners. But it also covers interesting topics for advanced data engineering professionals. Considered one of the best books for data science, it discusses how far experts expect the industry to advance. If you want to know what is in the future of deep learning and statistical learning this book is for you. 

4. Big Data: Algorithms, Analytics, and Applications

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

Big Data: Algorithms, Analytics, and Applications

This book compiles the knowledge of leading experts to give insight into deep learning. It covers algorithms and other computational methods. Broken into five different sections, the book explores:

  • Data applications
  • Data management
  • Data privacy
  • Data processing
  • Data stream techniques and algorithms

For each of the general topics, the book gives practical applications, models and techniques, and examples from the real world. The book provides data science knowledge for both aspiring data scientists and seasoned ones. 

5. Big Data and Social Science

Ian Foster and Rayid Ghani

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

This book draws on the expertise of figures working in statistics, data science, computer science, and the social sciences. Authors Ian Foster and Rayid Ghani discuss ways in which you can use data science to tackle real-world problems. The book is a practical guide to methods and tools used in deep learning. 

Written for both students and working professionals, the book provides its readers with the practical knowledge needed to combine various math and science disciplines in order to approach things like innovation. Whether you’re in a data science career or just starting your data science journey, this book can help. 

6. Big Data: Does Size Matter?

Timandra Harkness

Big Data: Does Size Matter

Those interested in the real-world application of data science will find this book by Timandra Harkness an intriguing addition to your collection of best business intelligence books. The author presents a variety of stories and examples of how deep learning has changed society. The author gives real accounts of people using data science. There are answers to valid questions, such as:

  • “Are you a data point or a human being?”
  • “How can deep learning change the world?”
  • “What are the unspoken assumptions underlying [data science and] its methods?”

7. Big Data for Business

Victor Finch

Big Data for Business

Victor Finch’s Big Data for Business is a must-read for business owners wanting to know more about data science. It also helps you understand how exploratory data analysis can help create customers and revenue.

Through unique insight and detailed real-world examples, readers learn how to overcome common challenges. They also learn how to use descriptive and predictive analytics. The book covers the most common business analytics tools used by data scientists. It also discusses various algorithms, data analysis techniques, and other fundamental concepts.

8. Big Data: How the Information Revolution is Transforming Our Lives

Brian Clegg

Big Data: How the Information Revolution is Transforming Our Lives

Brian Clegg’s Big Data: How the Information Revolution is Transforming Our Lives looks at various markets dominating our lives. It describes the impact data science has had on society. Readers of this book can gain insight into such questions as:

  • “Why do airlines overbook?”
  • “Was the Brexit vote successful data politics or the end of democracy?”
  • “How does data science enable Netflix to forecast a hit?”

As one of the best data science books out there offering real-world examples, it is good for beginners and experienced professionals. Learn data science skills, and how machine learning has changed our world. 

9. Big Data in Practice

Bernard Marr

Big Data in Practice

Big Data in Practice, written by bestselling author Bernard Marr, illustrates for readers the many ways in which major companies use machine learning and artificial intelligence. It covers just how important machine learning algorithms are to large companies collecting data. 

The book profiles companies ranging from Amazon and Target to John Deere and Apple. It details how each company uses data science capabilities to encourage:

  • Data science tools
  • Deep learning
  • Improvements of customer service
  • Innovation
  • Revenue
  • Statistical learning

This book is one of the most widely-used data science books out there. 

10. Big Data MBA

Bill Schmarzo

Big Data MBA: Driving Business Strategies with Data Science

Big Data MBA provides business strategies in data science for business owners. Bill Schmarzo’s book gives practical and proven framework. It offers the reader hands-on exercises and tips for using data to transform their business.

There is a long list of topics on how to learn data science. The list includes where and how to leverage statistical concepts and data and how to structure an organization to drive analytic insights. It also covers how to “think like a data scientist.” 

If you’re a budding entrepreneur, this book can help you understand practical statistics. It also covers machine learning algorithms and how to improve your data science skills. 

11. Big Data Science & Analytics: A Hands-On Approach

Arshdeep Bahga and Vijay Madisetti

Big Data Science & Analytics: A Hands-On Approach

Big Data Science & Analytics is the name of both this book and the “fourth industrial revolution” around which it is focused. This comprehensive yet crash course book covers a wide range of analytics topics. Discussions include:

  • How to analyze data in intelligent systems
  • Machine learning
  • Modern analytic methods
  • Specific software tools
  • Statistical analysis in data

The clear language in each of the book’s three parts makes it one of the best data science books for beginners. It offers helpful diagrams and code examples that make understanding tough topics easy. 

12. Big Data, Small Devices

Donna Governor and Michael Bowen

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

Written for teachers of Earth and environmental sciences, this fun and comprehensive book helps turn students into amateur data scientists. It is one of the easiest to read data science books out there. What makes it easy to understand? The book is full of tips for using existing websites and apps to track and detect patterns in the atmosphere, biosphere, and geosphere. It also shows how data scientists use data to track seasons.

Major topics the book covers through an investigation of the natural world include:

  • Data engineering
  • Deep neural networks
  • Exploratory data analysis
  • Machine learning
  • Natural language processing
  • Programming languages

Some consider this data science handbook a must-have for students. You might even use it to tackle a typical data science project.

13. Data Analytics

James Fahl

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

In this James Fahl book, readers get a step-by-step guide on analytics. It teaches you how to use practical statistics for data to increase revenue. Beginners will appreciate its solid summary of what data science and analytics are and why data science is important.

More advanced users will benefit from the easy-to-follow tips for understanding various models. The book also covers topics in:

  • Avoiding common mistakes
  • Collecting data
  • Data literacy
  • Deep learning
  • Machine learning

14. Data Analytics

Robert Keane

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

Robert Keane’s book is a helpful guide on understanding data in business. It gives business owners the edge they need to succeed in the marketplace. This book walks readers through how to get started with exploratory data analysis. It also offers the best ways to use predictive analytics.

In this book, readers learn various techniques for using machine learning and data science in their life. They learn how machine learning can predict and interpret statistics for data scientists. This is a top choice book for those seeking data science jobs or in a current data science career. 

15. Data Analytics: Practical Guide 

Arthur Zhang

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’s book is perfect for the reader who is new to the topic. It provides detail for the reader to gain a solid working knowledge of how data science and analytics can improve both business and life.

By the last page, readers will gain key insights into such topics as:

  • Data gathering and scrubbing
  • Data for small businesses
  • Deep learning
  • Descriptive modeling
  • Google trends
  • Machine learning techniques
  • Natural language processing
  • Programming language and machine learning

This practical guide helps readers leverage the power of algorithms and essential math concepts. Through all that this data science book offers, you learn how to improve your business, work, and life. It is one of the top data science books available. 

16. Data Science and Big Data Analytics

EMC Education Services

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

EMC Education’s book on data science focuses on ideas, principles, and applications. These are things that you can apply to any business or industry type.

This data science book walks its readers through a number of topics. It guides them toward becoming a contributor to data science by teaching them such concepts as:

  • Analyzing data
  • Data science interview questions
  • Essential math and algorithms
  • Machine learning and data
  • Storytelling with data science

17. Data Science

Doug Rose

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

Business managers who aspire to in-house data science, as opposed to hiring outside talent, will appreciate one of the most practical data science books out there. Data Science is a guide that takes readers through building an effective data science team.

In this book, you will find topics such as how to find existing talent within an organization. It also covers which roles to fill and their respective responsibilities. But topics explore more than how to create teams. It also covers important data science and machine learning concepts.

18. Data Science for Business

Foster Provost and Tom Fawcett

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

Written by two data science experts, Data Science for Business is commonly used as a textbook. It is one of the most recommended data science books. It teaches readers the basic principles of data science and walks them through “data-analytic thinking.” 

In this book, you learn how to think like a data analyst. Content of the book comes from the MBA course that one of the authors taught (Provost) at NYU over the past decade. The book teaches you how to participate in your company’s data science projects and come up with new and exciting ideas. 

19. Data Smart

John W. Foreman

Data Smart: Using Data Science to Transform Information into Insight

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! This is what John W. Foreman, author of the Amazon bestseller Data Smart says. His book walks readers through the new world data has created. It shows you how you can use the same strategies as big companies to benefit.

Topics covered in this book include:

  • Cluster analysis
  • How to use spreadsheets in data science
  • Machine learning
  • Optimization modeling
  • Organization modeling
  • Popular programming language in data science

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

Seth Stephens-Davidowitz

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

Everybody Lies, a bestseller on Amazon, uses data to ask interesting questions about human nature. It explores topics on what the internet can tell us about who we really are. Topics investigated include:

  • How and if you can beat the stock market
  • How often humans lie about their sex lives
  • Voting trends
  • Whether violent movies impact crime rates

Out of all the data science books available, this is one of the most unique to read. It focuses on more machine learning and data techniques. Its focus is a unique take on how we can use data to answer interesting questions. 

21. Getting Started with Data Science

Murtaza Haider

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

Written in the same style that made books like Freakonomics and Outliers popular, Getting Started with Data Science uses fascinating narratives to show readers what it takes to make big data data science work for them.

The book explores a wide range of topics. The topics form interesting questions about data science, including:

  • “Are religious individuals more or less likely to have extramarital affairs?”
  • “Does the presence of children influence a family’s spending on alcohol?”
  • “What determines housing prices more: lot size or the number of bedrooms?”

If you’re starting out as a beginner data scientist, this is one of the most fun data science books to read. 

22. The Intelligence Enterprise in the Era of Big Data

Venkat Srinivasan

The Intelligence Enterprise in the Era of Big Data

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 has become synonymous with business intelligence.

While the book might seem like a professional guide, it works for beginners because of the way it covers fundamental concepts. Both new and experienced data scientists can get a lot out of this book. 

23. Introducing Data Science

Davy Cielen, Arno Meysman, and Mohamed Ali

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

This bestselling big data book written by Python experts guides readers through the basic principles of data science. The book covers everything from machine learning to statistical concepts. It also stands as a Python data science handbook.

Why do Python users love this book? It discusses Python language and its common libraries. It also provides a helpful introduction to machine learning and shows readers how to handle large data. You also learn how to write data science algorithms.

24. Numsense! Data Science for the Layman

Annalyn Ng and Kenneth Soo

Numsense! Data Science for the Layman: No Math Added

Numsense! is a book about big data and data science. Stanford has used this book in its 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
  • Anomaly detection
  • Artificial intelligence
  • Clustering
  • Neural networks
  • Regression analytics
  • Social network analysis 

Good news about this data science book. It has no math. But it does cover data mining and other relevant topics. 

25. The Rise of Big Data Policing

Andrew Guthrie Ferguson

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

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 changes the way police do their jobs. It also addresses why citizens should be aware of these changes.

In this book, readers get a glimpse of what the future holds for machine learning and data. The author reveals how new technologies impact society at large and your own data (personal). It is a must-read for anyone concerned with how technology can revolutionize policing and law enforcement.

26. Storytelling with Data – A Guide for Business Professionals

Cole Nussbaumer Knaflic

Storytelling with Data: A Data Visualization Guide for Business Professionals

This Amazon bestseller is a must-read for any data scientist or those interested in big data visualization and analytics. The book walks readers through the steps of visualizing data and using it to communicate. It informs readers how to tell a story using data.

Readers finish the book having learned necessary skills for the industry. They learn how to:

  • Determine appropriate graphs for various situations
  • Direct an audience’s attention to the most important parts of data
  • Focus on deep learning in the data science field
  • Recognize what’s important data and what is clutter

27. Too Big to Ignore 

Phil Simon

Too Big to Ignore: The Business Case for Big Data

In Too Big to Ignore, author Phil Simon looks at dozens of real-world examples to summarize the evolution and future of data. The book is a must-read for those in the early stages of figuring out what data science is and how they can put it to use. It is a best computer for data science book in academia and the professional world.

The book answers interesting questions like why insurance companies track real-time customer driving patterns. It also addresses why citizens report potholes on their smartphones, among other topics. 

28. Weapons of Math Destruction

Cathy O’Neil

Weapons of Math Destruction

Author Cathy O’Neil, a former Wall Street tycoon, wrote this bestselling book about the ways algorithms change the world. But not all changes are good. In this book, the author describes different ways data has come to determine change.

Some of the topics covered in this book include:

  • How employers sort through resumés
  • How insurance companies monitor our health
  • If a student from a poor zip code can get a college loan

The book takes a hard look at situations from the dark side of data science.

29. What Every Manager Should Know About Big Data 

Lars Nielsen and Noreen Burlingame

What Every Manager Should Know About Big Data and Data Science

This book focuses on the real-time business intelligence aspect of data science. Aimed at business managers, it details the need-to-know basics of data science, how it can enhance business, and the processes by which it functions. The best data science books provide application as well as theory. This book combines both elements. 

The book covers both the whats and whys of data science. It often gets comped to R for Data Science book by Garrett Grolemund and Hadley Wickham. But R for Data Science doesn’t cover as much of the management portion of the industry as this book does. 

30. What To Do When Machines Do Everything

Malcolm Frank and Paul Roehrig

What To Do When Machines Do Everything

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

In this book, you learn how to get ahead in the world of:

  • Artificial intelligence
  • Algorithms
  • Bots
  • Data 
  • Machine learning

It also discusses practical statistics for data and statistics for data scientists. 

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