10 Best Data Science Books You Should Read in 2024

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10 Best Data Science Books You Should Read in 2024

Ramya Shankar
Last updated on June 16, 2024

    With data science being one of the most flourishing sectors, getting well-versed in it offers many career opportunities. If you plan to learn it from the ground up, books are the best place to start. Continue reading this article to find out the best data science books.

    The floods of data are available in this modern digital age, and companies are heavily relying on it. Data has become the lifeblood and driving force of almost every industry, from healthcare and manufacturing to banking and government agencies.

    Well, the data generated is in a raw and unstructured format. The role of data science comes into play to transform raw and noisy data into knowledge.

    The field of data science is more innovative, challenging, and even lucrative. As there are humongous amounts of data generated daily, the demand for data professionals, like data scientists, data analysts, data engineers, etc., is at its peak. So, having data science skills can land you a good, high-paying job in a renowned company.

    Do you know?

    According to the US Bureau of Labor Statistics, the job outlook of data scientists is likely to grow by 36% from 2021 to 2031 .

    That being predicted, learning data science would offer even more opportunities in the future. Hence, learning and mastering it will help you shape your career.

    Many beginners start learning with online courses, tutorials, YouTube videos, etc. But, along with it, for a more serious, detailed, and long-term learning experience, it is always better to go with books. Doing so complements what you learn via other modes. As a plus, a book is also an excellent way to get away from gadgets for some time.

    Now, let us explore some of the best data science books for beginners and advanced learners.

    10 Best Data Science Books To Try in 2024 [Beginners & Advanced]

    1. Data Science from Scratch

    Data Science from Scratch- First Principles with Python

    Author: Joel Grus

    It serves as the first data science book for many and an indispensable reference to several others. If you have read a few blogs and have a fundamental knowledge of the field, this book is a great place to start. You will be taken to a different universe of data scientists, where a single problem will lead you to think in many ways about data.

    The book further teaches you the basics of Python in addition to statistics, linear algebra, probability, ML, and practically everything that you need to learn in data science. It follows a do-it-yourself approach to enhance your thinking capabilities while guiding you to do what the author did differently. You can find multiple code examples to improve your coding skills.


    • Ideal for beginners.
    • A quick course on Python, the most popular programming language for data science.
    • The book is self-explanatory and easy to follow. You need no other resources except those mentioned in the book.
    • An in-depth exploration of ML concepts and other areas of data science.

    About the Author

    Joel Grus is a research engineer at Allen Institute for Artificial Intelligence. Previously, he worked as a data scientist for multiple startups and as a software engineer at Google.

    Publisher: O'Reilly Media
    Paperback Print Length: 406 pages

    You can buy this book here .

    2. Data Science for Business

    Data Science For Business

    Author: Foster Provost and Tom Fawcett

    This is an ideal book for absolute beginners as well as those who have some idea about data science. If you are not convinced that data science is the right choice for you, this book will change your mind by giving practical examples of how data is mined and analyzed to achieve different results. The book first provides a high-level overview of the applications of data science and then moves on to technical details.

    The data science book is less about the technicalities of data science and more about thinking, creating, and analyzing business problems. Rather than going in-depth into the code details, Data Science for Business explains the fundamental concepts beautifully.

    Major Features of Data Science for Business:

    • Ideal for beginners and learners with some experience with data science.
    • Great for technical as well as business people.
    • Gives an overall picture of data science and then goes into details.
    • Topics are explained in an easy-to-understand manner with examples.
    • The pace is perfect, and the content never feels overwhelming.

    About the Author

    Foster Provost is a professor at the NYU Stern School of Business. He specializes in teaching data science, MBA, and business analytics. Moreover, he is the co-founder of several companies focusing on data science.

    Tom Fawcett holds a Ph.D. in machine learning. He worked for more than 20 years in several companies, such as HP Labs, NYNEX/Verizon Labs, and GTE Laboratories.

    Publisher: O'Reilly Media
    Paperback Print Length: 414 pages

    You can buy this book here .

    3. Data Smart – Using Data Science to transform information into insight

    Data Smart: Using Data Science to Transform Information into Insight

    Author: John W. Foreman

    The next entry on our list of the best data science books is Data Smart. This data science book starts with a brief introduction to data science. It then quickly moves on to details of ML algorithms, forecasting, and analysis and uses R in the later chapters to do some data-science-related programming.

    Until then, you have to grasp the concepts without any burden of coding. The data science book starts with an example of data that is in Microsoft Excel. Hence, knowing the basics of Excel is a prerequisite for you to pick Data Smart for learning data science.

    In the first chapter itself, you will get a good overall picture of data science, from data transformation to visualization using different features of Excel. You can read this book and then follow it with some courses on statistics, R, or ML to further strengthen the concepts taught in the book.

    Major Features of Data Smart:

    • Uses witty language and a compelling introduction to concepts.
    • Step by step learning by carefully creating the overall picture and then going into details.
    • No need to know any new programming language or tools like most of us have worked on Excel at some point.
    • Following a practical approach rather than too much reliance on mathematical theory or notations makes it an easy read, even for those without a programming background.

    About the Author

    John W. Foreman is the data scientist for Mailchimp . He is also a management consultant who worked for many companies and government agencies, where he has done a lot of analytics work.

    Publisher: Wiley
    Paperback Print Length: 432 pages

    You can buy this book here .

    4. Python Data Analytics

    python data analytics: how to learn data science and use machine learning introduction to deep learning to master python for beginners

    Author: Tony F. Charles

    This book is a perfect way to start your data science journey. If you have never read about or worked with data science, this is a must-have book for you. It completely focuses on Python and how the language can be used extensively for data science. Python Data Analytics spoon-feeds every step of programming with Python and deals with a lot of packages like NumPy and SciPy that are used exclusively for analyzing and visualizing data.

    This Python programming book teaches you data science and Python. The author explains all the concepts in simple and straightforward language. Moreover, the examples help to understand various concepts thoroughly.

    Major Features of Python Data Analytics:

    • Completely covers Python, the most preferred programming language for data science.
    • Great for beginners who want to build a strong foundation in both data science and Python.
    • Loads of examples, tutorials, practical exercises, and explanations on Python data science libraries .
    • Comprehensive and contains a good variety to keep the reader engaged.
    • The book also introduces TensorFlow while teaching about ML algorithms.

    About the Author

    Tony F. Charles is the author of many best-selling Python books , including Python Machine Learning, Python for Beginners, and Python for Data Science.

    Publisher: Independently published
    Paperback Print Length: 176 pages

    You can buy this book here .

    5. R in Action

    R in Action: Data Analysis and Graphics with R

    Author: Dr. Rob Kabacoff

    While most books you would find on data science are based on Python, R is an equally powerful language for the same. This book starts with a basic course on R and the different R packages that are useful for data science and then gradually moves on to other concepts in a logical order so that beginners can fully understand the entire process.

    The parts of data management and statistics in R in Action are quite detailed and well-organized for both beginner- and intermediate-level data scientists and business analysts. The range of topics covered is quite wide, and yet there is no prerequisite for reading this book, which makes it one of the best data science books.

    Major Features of R in Action:

    • Shows the power of the R language with practical and relatable examples.
    • Each chapter covers one algorithm or topic at length.
    • The book touches upon advanced data science concepts as well.
    • Covers both basic and advanced statistical methods as well as graphics along with real-world examples.
    • Suitable for both beginners and experienced users.

    About the Author:

    Dr. Rob Kabacoff is a seasoned researcher and teacher whose expertise is in data science.

    Publisher: Manning
    Paperback Print Length: 608 pages

    You can buy this book here .

    6. Data Science for Dummies

    Data Science For Dummies, 2nd Edition (For Dummies (Computers))

    Author: Lillian Pierson and Jake Porway

    This book doesn’t go much into the details but touches upon all the topics comprehensively, making it one of the most comprehensive data science books. It is an ideal data science book to know the vastness of data science and the concepts involved with big data. Moreover, it serves well as a quick reference when you are stuck or need to look up something. The book’s tone is friendly, witty, and funny.

    Thus, it keeps you hooked. Data Science for Dummies introduces loads of data science tools that are much useful for performing data analysis and visualization. It also dedicates a whole chapter to the applications of data science, wherein readers can understand the importance of learning data science and how it can be applied to solve their business problems.

    Major Features of Data Science for Dummies:

    • A handy reference that covers all the concepts of data science.
    • Encompasses the entire scope of data science, thus making it suitable for beginners and intermediate learners.
    • Covers the basics of many important data science tools, like D3, R, Python, SQL, Excel, KNIME, Excel, MapReduce, Tableau, SVG, and Weka.
    • Includes a handy data science cheat sheet as well as some datasets that you can use for practice.
    • Part 5 of the book focuses on a few domains (journalism, environment, e-commerce, and so on) and covers the related use cases in-depth for comprehensive learning.

    About the Author

    Lillian Pierson is a data scientist and professional environmental engineer. Moreover, he is a data science consultant to prestigious media corporations, tech giants, and major governmental and non-governmental agencies.

    Jake Porway is a technology and machine learning enthusiast and loves finding good values in data. He is the founder and executive of Datakind, a data science company.

    Publisher: For Dummies
    Paperback Print Length: 384 pages

    You can buy this book here .

    7. Data Science for the Layman

    Numsense! Data Science for the Layman: No Math Added

    Author: Annalyn Ng and Kenneth Soo

    On number 7 of our list of the best data science books, we have Data Science for the Layman. This book is a great read for developers as well as business analysts. It is crisp and explains the concepts of statistics and data mining subtly from the first chapter itself.

    The author doesn’t waste much time on theory and starts with a practical, easy-to-understand example to cover the topics step by step. In most reviews, you would have read that this is an entry-level book; however, the book also touches upon some important advanced data science concepts like neural networks and supervised and unsupervised learning .

    Major Features of Data Science for the Layman:

    • Doesn’t involve any coding, implementation, or programming language usage.
    • Excellent informational content for business leaders and managers to understand data science concepts.
    • The well-organized flow of concepts helps you to mentally picture how the entire process works without having to code or see the actual results.
    • The book helps you develop an analytical mindset and enables you to think how a data scientist would and create questions that are helpful from a business perspective.
    • Follows a simple writing style.

    About the Author

    Annalyn Ng is a data scientist at Amazon Web Services (AWS). She holds an MPhil degree from the University of Cambridge. Also, she was an undergraduate tutor at the University of Michigan.

    Kenneth Soo holds an MS degree in statistics from Stanford University. He was a research assistant with the Operational Research & Management Sciences Group at the University of Warwick.

    Publisher: Annalyn Ng and Kenneth Soo
    Paperback Print Length: 145 pages

    You can buy this book here .

    8. Data Science and Big Data Analytics

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

    Author: EMC Education

    Services The book is exhaustive and covers each aspect of data science with examples and case studies. The way the examples are presented is excellent, which is the single major reason to put Data Science and Big Data Analytics among the league of best data science books. Data Science and Big Data Analytics is for everybody.

    It starts with basics and then goes up to explain advanced concepts simply. There are a lot of colorful illustrations and pictures that make the book further appealing and an interesting read.

    Major Features of Data Science and Big Data Analytics:

    • Uses R as the base programming language.
    • Introduces a good balance of mathematical concepts and advanced ML algorithms.
    • The code and datasets can be easily downloaded from the links provided (Wiley website).
    • Though a little too technical, the author has tried to explain basic concepts very well to keep them interesting for readers.
    • If you are planning for a data science certification, this book will certainly get you there.

    About the Author

    EMC is a leader in enabling businesses and service providers to transform their operations and service IT as a service.

    Publisher: Wiley
    Paperback Print Length: 432 pages

    You can buy this book here .

    9. Designing Data-Intensive Applications

    Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

    Author: Martin Kleppmann

    This data science book is a purely technical resource, authored to help software engineers and architects build applications using the best tools. Designing Data-Intensive Applications discusses various tools and the use cases where each tool is the best one.

    The author breaks down all the complex concepts into small bits that are easy to understand. You will feel as if the author knows the next question in your mind and answers it just when you are pondering about it.

    Major Features of Designing Data-Intensive Applications:

    • Coherent and explains even complex topics in a simple manner.
    • Emphasizes the importance of using the right data structures for different types of problems.
    • Not just implementation, the book also focuses on why data is so important today and how it impacts the overall business.
    • It helps the readers to develop a creative and analytical mindset by thinking beyond the implementation and answering questions like why, when, whom, where, and how.
    • The book also focuses on performance, security, and the need to develop systems with sturdy architecture by including dedicated chapters for these.

    About the Author

    Martin Kleppmann is a researcher in distributed systems at the University of Cambridge. Formerly, he was a software engineer and entrepreneur at leading Internet companies, such as LinkedIn and Rapportive.

    Publisher: O'Reilly Media
    Paperback Print Length: 611 pages

    You can buy this book here .

    10. Pattern Recognition and Machine Learning

    Pattern Recognition and Machine Learning (Information Science and Statistics)

    Author: Christopher M. Bishop

    Most of the previously-mentioned books are for beginners and intermediate learners; however, this book is for experts. It contains in-depth information about topics that most other data science books won't have. Pattern Recognition and Machine Learning is an exhaustive read and will challenge you at all levels for good.

    The author explains graphical modeling and pattern recognition with loads of mathematical equations, although you wouldn't find any practical examples. This book is for serious learners and focuses on ML and not on the overall ecosphere of data science.

    Major Features of Pattern Recognition and Machine Learning:

    • Prior basic knowledge of statistics and algebra is a must.
    • Crystal clear and thorough explanation of advanced concepts.
    • There are loads of equations, but you will find them interesting rather than overwhelming.
    • Some parts or sub-topics are left unexplained. Still, those are easy to find through other sources like the internet and books.
    • The book encourages self-learning and analysis of concepts leaving it to the reader to think analytically and arrive at solutions.

    About the Author

    Christopher M. Bishop is Laboratory Director and Microsoft Distinguished Scientist at Microsoft Research Cambridge. Moreover, he is a fellow of Darwin College Cambridge. He is also a professor of Computer Science at the University of Edinburgh.

    Publisher: Springer
    Paperback Print Length: 738 pages

    You can buy this book here .


    In our experience, rather than jumping into the fine technical details of each subtopic in the first go itself, it would be nice to understand why data science is a good choice and how the whole thing works from both technical and business perspectives.

    The overall picture will help you choose what's most important and where your interest lies. In our list, Data Science for Business and Python Data Analytics are two books that can give you a good start.

    If you have read about data science from blogs or completed the basic courses, you might want to go with Data Science from Scratch or Data Science for Dummies, followed by the other best data science books in no particular order. Each book focuses on different parts of data science and will help you gain various perspectives.

    Happy reading!

    People are also reading:


    If you want to get started with data science and learn the fundamentals, Data Science from Scratch by Joel Grus and Data Science for Business by Foster Provost and Tom Fawcett are the best ones.

    To learn Python for data science, you can read Python Data Analytics by Tony F. Charles and Data Science from Scratch by Joel Grus.

    To get a better grasp of R, the best book is R in action by Dr. Rob Kabacoff.

    Though data science professionals are not assigned the task of coding, they should possess knowledge of various popular programming languages, such as Python, R, MATLAB, and SQL. Python and R are the most preferred languages for data science.

    Python, R, Scala, SQL, Julia, MATLAB, C/C++, Java, and JavaScript are some of the best languages for data science.

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