Updated: May 10, 2023

13 Best Data Science Books to Read in 2023

Here is our list of the best data science books.

Data science books are books that provide techniques and insights to help readers understand the different aspects of data science. Examples of data science books include The Data Warehouse Toolkit by Ralph Kimball and Margy Ross and Building Machine Learning Powered Applications: Going from Idea to Product by Emmanuel Ameisen. The purpose of data science books is to help readers acquire the necessary skills in all fields of data science, such as data cleaning, data interpretation, and data modeling.

This list includes:

  • data science management books
  • data science books for beginners
  • advanced data science books
  • books about data science

Let’s get started!

List of data science books

Data science books provide valuable knowledge and resources to individuals seeking to learn about data science and its application. Here is a list of data science books you should consider reading.

1. The Data Warehouse Toolkit by Ralph Kimball and Margy Ross

One essential component of modern data management is data warehousing. The Data Warehousing Toolkit provides a comprehensive guide to building and designing data warehouses. This book covers many details, from basic concepts to advanced techniques. Ralph Kimball and Margy Ross are renowned experts in data warehousing. As a result of their expertise, these authors adopted a practical approach throughout the book, using real-life examples and case studies to illustrate all techniques and concepts. The Data Warehouse Toolkit is suitable for technical and nontechnical readers and would benefit any individual interested in data warehousing.

Notable quote: ”Simplicity is the fundamental key that allows users to easily understand databases and software to efficiently navigate databases.”

Check out The Data Warehouse Toolkit.

2. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett

In this book, data science experts Foster Provost and Tom Fawcett introduce the fundamental principles of data science. Data Science for Business touches on data-analytic thinking required for obtaining business value from data collection. This book provides real-world examples of business problems. Also, Data Science for Business helps readers improve communication between data scientists and business stakeholders while participating in data science projects. With Provost and Fawcett’s expertise, readers will learn various methods of utilizing data science in business decision-making.

Notable quote: ”It is important to understand data science even if you never intend to apply it yourself.”

Check out Data Science for Business.

3. Building Machine Learning Powered Applications: Going from Idea to Product by Emmanuel Ameisen

Building Machine Learning Powered Applications is one of the best data science management books. Emmanuel Ameisen is a data scientist who focuses on the practical applications of machine learning. Machine learning is one of the most exciting aspects of data science with many applications. The book provides a comprehensive guide to building applications powered by machine learning. One key strength of this book is the author’s focus on practical applications of machine learning. Ameisen explains how to preprocess data to ensure suitability for machine learning by adopting feature scaling, feature engineering, and data cleaning. These features are essential for building reliable and accurate models.

In addition, the book touches on deploying machine learning models using popular frameworks such as Docker and Flask. This book is easy to follow for beginners and machine learning experts alike.

Notable quote: ”Machine learning is powerful and can unlock entirely new products, but since it is based on pattern recognition, it introduces a level of uncertainty.”

Check out Building Machine Learning Powered Applications.

4. Data Science from Scratch: First Principles with Python by Joel Grus

Using the Python programming language, Data Science from Scratch provides a practical introduction to data science. The book covers the topic of data cleaning, machine learning, deep learning, and data science. By focusing on real data science problems, this book touches on essential concepts by implementing solutions through coding and statistics. While knowledge of Python is optional to get results from this book, some knowledge in the field will make learning easier. For beginners with no real-life experience dealing with ethical issues, Data Science from Scratch includes a chapter on ethics in data science. Furthermore, this book introduces readers to statistics, probability, and linear algebra concepts before delving into more advanced topics like deep learning and machine learning.

Notable quote: ”Data science is a hot and growing field, and it doesn’t take a great deal of sleuthing to find analysts breathing prognosticating that over the next 10 years, we’ll need billions and billions more data scientists than we currently have.”

Check out Data Science from Scratch.

5. Numsense! Data Science for the Layman: No Math Added by Annalyn Ng and Kenneth Soo

Annalyn Ng and Kenneth Soo provide a clear introduction to data science concepts without a background requirement in statistics or mathematics. By using simple and everyday examples, Ng and Soo break down complex topics for readers with limited knowledge of the subject. Also, Numsense! Data Science for the Layman emphasizes the importance of collaboration and communication in data science. Making a successful data-driven decision requires multiple stakeholders, so the authors provide adequate guidance on effectively communicating data insights to other collaborators.

Notable quote: ”To appreciate how data science is driving the present data revolution, there is a need for the uninitiated to gain a better understanding of this field.”

Check out Numsense! Data Science for the Layman: No Math Added.

6. The Art of Data Science: A Guide for Anyone Who Works with Data by Roger Peng and Elizabeth Matsui

In The Art of Data Science, Roger Peng and Elizabeth Matsui focus on their experiences to coach managers and beginners in analyzing data science. Peng and Matsui are both experts in data and analyst management in the professional space. As a result, these two provide knowledge that will yield successful results and share what pitfalls to avoid in the process. The Art of Data Science prioritizes the importance of the human element in data science. Successful data science requires technical, problem-solving, communication, and collaboration skills. This book provides readers with practical knowledge on approaching data science from a human-centric perspective. Readers will also gain insights on understanding stakeholders’ goals and needs and building credibility and trust in the process.

Notable quote: ”Data analysis is hard, and part of the problem is that few people can explain how to do it.”

Check out The Art of Data Science.

7. Data Science for Dummies by Lillian Pierson

Lillian Pierson is an expert at breaking down complex subjects into simpler forms, and her book, Data Science for Dummies, is no exception. This book focuses on the business side of data science and provides an introductory guide to starting in the field as a professional. The publication is one of the best data science books for beginners to get familiar with the concepts of big data and the application of data science to everyday life. The book also explores machine learning, artificial intelligence, data visualization techniques, data engineering, and algorithms. Pierson provides an overview of data visualization techniques and tools and how these tools can help effectively communicate data insights. Data Science for Dummies provides an introduction to data science and a reference for professionals who want to improve their skills.

Notable quote: ”Data science and artificial intelligence have disrupted the business world so radically that it’s nearly unrecognizable compared to what things were like just 10 or 15 years ago.”

Check out Data Science for Dummies.

8. Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support by Ramesh Sharda, Dursun Delen, Efraim Turban, and David King

This textbook covers various artificial intelligence, data science, and analytics topics. Analytics, Data Science, and Artificial Intelligence is one of the advanced data science books that explores the connection between different parts of an organization and the impact of decisions on the overall system. Also, the authors touch on ethical considerations in data science with guidance on handling potential decision-making issues. This book explores several case studies and how different technologies can help improve systems performance. Analytics, Data Science, and Artificial Intelligence is a valuable book for professionals and students looking to develop skills across various data science and management areas.

Notable quote: ”Analytics has become the technology driver of this decade.”

Check out Analytics, Data Science, and Artificial Intelligence.

9. Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schönberger and Kenneth Cukier

Viktor Mayer-Schönberger and Kenneth Cukier discuss the effect of data on every aspect of human life, including business, personal, government, and scientific disciplines. Big Data focuses on how algorithms can reveal information about humans by analyzing habits online. For example, online retailers can predict consumers’ buying patterns based on browsing, and dating apps use data to shape love lives. Big Data is a great management science book for readers looking to understand the potential of big data and how this data can drive growth and innovation. For executives and managers who wish to build a successful data strategy, Big Data is an excellent starting point.

Notable quote: ”Big data refers to our newfound ability to crunch a vast quantity of information, analyze it instantly, and draw sometimes astonishing conclusions from it.”

Check out Big Data.

10. Doing Data Science: Straight Talk from the Frontline by Cathy O’Neil and Rachel Schutt

Doing Data Science is a practical book that provides a comprehensive introduction to data science, including concepts, tools, and techniques used in the industry. Cathy O’Neil and Rachel Schutt’s approach makes this book an excellent resource for beginners exploring data science and its application in business. With a focus on real-world examples and case studies, Doing Data Science illustrates the application of data science in solving complex business problems. O’Neil and Schutt provide practical advice on approaching data science projects, defining the problem, collecting and cleaning data, and developing and testing models.

Notable quote: ”The world is opening up with possibilities for people who are quantitatively minded and interested in putting their brains to solve the world’s problems.”

Check out Doing Data Science.

11. Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics by Thomas Nield

Essential Math for Data Science helps readers master statistics, machine learning, and data science skills. In this book, Thomas Nield guides readers through topics like linear algebra, probability, logistic regression, calculus, and neural networks. In addition, Nield shares practical insight into the state of data science and how to apply these insights in maximizing a career in data science. Essential Math for Data Science will guide you through learning data science while avoiding common pitfalls and biases.

Notable quote: ”Number theory goes all the way back to ancient times when mathematicians studied different number systems, and it explains why we have accepted them the way we do today.”

Check out Essential Math for Data Science.

12. Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning by Alex J. Gutman and Jordan Goldmeier

In Becoming a Data Head, Alex J. Gutman and Jordan Goldmeier dig deep into data science to provide the necessary tools for success in the field. Gutman and Goldmeier teach readers how to ask the right questions about statistics and result in the workplace, understand machine learning, and avoid pitfalls when interpreting data. Becoming a Data Head explores the topics of data visualization, machine learning algorithm, preprocessing, and statistical analysis. This publication is one of the best data science books for beginners.

Notable quote: ”To become better at understanding and working with data, you will need to be open to learning seemingly complicated data concepts.”

Check out Becoming a Data Head.

13. Data Analytics for Absolute Beginners: A Deconstructed Guide to Data Literacy by Oliver Theobald

Data Analytics for Absolute Beginners teaches the basic algorithms to think like a data scientist. Oliver Theobald adopts the Lego-set approach in each chapter and connects individual blocks to build knowledge. With Data Analytics for Absolute Beginners, beginners can go from having zero knowledge to becoming a pro at analyzing and discussing data problems. With a hands-on approach to learning, Theobald uses practical and visual examples to walk readers through the concepts. This book covers when and how to use classification clustering, natural language processing and regression analysis, and how to make better business decisions using data visualization.

Notable quote: ”Data takes the form of everything from words in books to sales logged in spreadsheets, as well as text and images contained in social media posts.”

Check out Data Analytics for Absolute Beginners.

Conclusion

Data science is one of the highest-paying fields of data, and this field will continue to thrive and innovate. The best way to stay on top of your game is by reading books about data science. Reading these books will provide a holistic view of the field. Contrary to what some might believe, data science is open to more than just computing. Data science also includes programming, machine learning, statistics, probability, and mathematics. Thriving in these fields requires adequate knowledge, and data science books will equip you with this information.

Although several books are available on data science, you do not have to read them all. The authors designed these books to appeal to different audiences, from beginners to advanced. You should read a book that appeals to your current experience level and build up from there.

FAQ: Data science books

Here are frequently asked questions about data science books.

What is data science?

Data science combines principles and practices from various fields of statistics, artificial intelligence, computer engineering, and mathematics to analyze large chunks of data. The analysis helps data scientists ask and answer major questions about what happened, why it happened, and what to do with the results.

What are data science books?

Data science books are valuable resources for experts and beginners in data science. These books provide a comprehensive understanding of the various aspects of data science, such as machine learning, data visualization, data analysis, and statistical methods. Field experts put together data science books that include theoretical and practical applications.

What are the best books about data science?

The best data science books include:

  • Becoming a Data Head by Alex J. Gutman and Jordan Goldmeier
  • Doing Data Science by Cathy O’Neil and Rachel Schutt
  • Big Data by Viktor Mayer-Schönberger and Kenneth Cukier
  • The Data Warehouse Toolkit by Ralph Kimball and Margy Ross

These books help educate readers of all experience levels on data science information and strategies.

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People & Culture Director at teambuilding.com.
Grace is the Director of People & Culture at TeamBuilding. She studied Industrial and Labor Relations at Cornell University, Information Science at East China Normal University and earned an MBA at Washington State University.

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