10 Best Data Science Books

To become a master in data science, you must often apply it in practice. Yet, you must have a solid knowledge base to handle mistakes in the process.

Besides academic theories in school, data science books are an endless source of information for you to look up what is related to them.

The ten books are the ones I have personally experienced. My objective reviews will help you make the right choice for your needs.

1. Head First Statistics

Head First Statistics – Dawn Griffiths


  • Paperback: 718 pages
  • English language
  • Weight: 2.85 pounds
  • Dimensions: ‎8 x 1.5 x 9.25 inches

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Reason To Buy:

  • Tight format layout
  • Close content
  • Lots of graphics and charts
  • Extensive knowledge


Head First Statistics is a book about data science with a clear layout for each reading stage. The book starts from the basics, including descriptive statistics, probability, distribution, and inferential statistics.

The books related to data science are highly academic and quite dull with numbers. When I read them, I found them particularly easy to understand.

The author often takes real-life examples related to casinos or things close to life for readers to access quickly.

Although they are just everyday examples, they give highly accurate statistics. Its primary purpose is to help readers absorb academic knowledge most simply.

In addition, the data mentioned in the book are represented by graphs. Therefore, you can directly understand and create a habit of looking at the data when working in reality.

2. Data Science from Scratch

Data Science from Scratch: First Principles with Python – Joel Grus


  • English language
  • Paperback: 406 pages
  • Weight: 1.4 pounds
  • Dimensions: ‎6.9 x 0.9 x 9.1 inches

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Reason To Buy

  • Python Acceleration Course
  • Dive into the basics of machine learning
  • Summary of linear algebra and statistical probability


Even though it’s just a book, it feels like a natural course. The content mainly focuses on knowledge related to python programming language, linear algebra, statistics, and probability.

The information is quite overwhelming, but the author presents it through humor in the highly academic section. So, you will find it clear and precise.

This book is the foundation for taking up higher data science levels. Therefore, it is especially suitable for those who are about to change careers or are just starting to learn about the python programming language.

3. Data Science For Dummies

Data Science For Dummies – Lillian Pierson


  • English language
  • Paperback: 408 pages
  • Weight: 0.035 ounces
  • Dimensions: ‎7.4 x 0.78 x 9.3 inches

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Reason To Buy

  • Easy to understand
  • Combined mining of big data topics
  • Details of visualization techniques
  • Critical thinking and in-depth business perspective


Data science is a multi-content field. It gives you the academic knowledge you need, such as programming languages, probability statistics, and data analysis. In its second half, you also learn more about the expanding data science space.

Its main content includes business, application, and big data frameworks such as Hadoop, MapReduce, and Spark. You also get preliminary instruction in data engineering and data science.

Because the chapters in this book are extensive, it will help you to become a master in this area. If you need to learn programming fundamentals or are looking for the basics of data science, this book is not the best choice.

In other words, Data Science For Dummies is suitable for intermediate and advanced programmers.

4. Designing Data-Intensive Applications

Designing Data-Intensive Applications – Martin Kleppmann


  • English language
  • Paperback: 1051 pages
  • Publisher: O’Reilly Media

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Reason To Buy

  • Famous author
  • The strengths and weaknesses of many tools
  • Detailed information about the systems in use
  • Show how to build a modern database


The book deals with data-intensive application designs for beginners and those wishing to consolidate their knowledge.

The book shows you how to choose different technologies for each specific case. It talks little about programming principles. However, the author delves into data types such as SSTables and B-trees.

To this day, it is the most objective book I have ever read. The author describes the pros and cons of the most popular applications.

From there, the book gives you accurate advice on choosing the correct application when going into practice.

5. Big Data

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


  • English language
  • Paperback: 272 pages
  • Weight: 7.5 ounces
  • Dimensions: ‎5.31 x 0.77 x 8 inches

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Reason To Buy

  • Good price
  • Diverse data sources
  • Logical and scientific presentation
  • Examples, benefits, and limitations of big data


It is the best book on big data. It does not introduce or refer to in general. Instead, the author provides evidence and analyzes such data’s challenges, benefits, limitations, and dangers.

In the final chapter, the writer discusses the dangers of relying on predictive algorithms, especially in criminology, criminal justice, and the police.

Plus, it’s also highly suitable for those interested in learning about machine learning and data roles. The examples the author gives are the ultimate goal of significant data discovery.

The book focuses on examples and conclusions rather than implementation.

6. Storytelling with Data

Storytelling with Data: A Data Visualization Guide for Business Professionals – Cole Nussbaumer Knaflic


  • English language
  • Paperback: 288 pages
  • Weight: 1.46 pounds
  • Dimensions: 7.3 x 0.6 x 9.2 inches

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Reason To Buy

  • Identify the appropriate chart
  • Learn how to create a compelling story
  • Easy-to-understand 3D charts
  • Easy-to-see presentation


Storytelling with data is a resource to help you build data visualization principles and learn how to communicate with data. Inefficient graphs are simple, easy-to-understand 3D pie charts.

Communication is not an inherent or natural skill you must practice daily, especially in the data field.

It is a fact that numbers in data science are specialized and very dry. However, with his experiences, the author will help you transform data into stories that are impactful, inspiring, and relatable to your audience.

If you’re looking to give a speech in an auditorium, it’s the right book for you as a guide to data storytelling.

7. Data Science and Big Data Analytics

Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data – EMC Education Services


  • English language
  • Paperback: 399 pages
  • Publisher: Wiley

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Reason To Buy

  • Simple and easy to understand
  • Implement a data analytics approach
  • Presentation content goes from basic to advance


Its content covers many issues, including the analysis life cycle, R introduction, and advanced methods.

When I read this book, I was looking at the big picture of data science. The book uses numbers and mathematical formulas to provide k-means analysis and linear regression. However, it’s not dry or makes it hard for the person to understand.

The book’s knowledge is general and extremely extensive, but in particular, it focuses more on R code than on learning python. So, if you need a Dell EMC DECA-DS certificate, this is a valuable source of information for you to exploit.

8. Data Science for Business

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


  • English language
  • Paperback: 413 pages
  • Weight: 1.5 pounds
  • Dimensions: ‎7 x 0.9 x 9.19 inches

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Reason To Buy

  • Profit curve
  • Reader-friendly
  • Real-life examples
  • Detailed explain


Right in the title, you may have partially understood the content of this book. It focuses on data science for business, from introducing fundamental data science principles to guiding you through data analytics thinking.

From there, you will derive valuable experiences and create a lot of business value from your collected data. The book also briefly mentions how to improve communication in business and tips for participating in data science projects.

The author goes from scarce data to a matter-of-essence presentation. After reading this book, it will help you to grow stronger on your data analysis journey.

9. R for Data Science

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data – Garrett Grolemund, Hadley Wickham


  • English language
  • Paperback: 518 pages
  • Weight: 1.64 pounds
  • Dimensions: ‎6 x 1.05 x 9 inches

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Reason To Buy

  • Clear section layout.
  • Provide an overview of R
  • Mention many other things.


Although many books that deal with R are available today, most need to be more specific. Yet, with R for Data Science, the book introduces you to R, Rstudio, a collection of R packages, and how to use R to turn raw data into insights.

By the time you’ve finished reading, you’ll know how to organize and transform your datasets, synthesize R tools for data problem-solving, and communicate and test data.

The book is divided into five main parts and develops into the focus, eliminating the tasteless and redundant things.

Although the book has an online version, you should read it to the reader to feel its authenticity and make it easier for you to read. The book is suitable for those looking to learn from basic to advanced R.

10. Hands-On Machine Learning

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems – Aurélien Géron


  • English language
  • Paperback: 856 pages
  • Weight: 2.8 pounds
  • Dimensions: ‎7 x 1.2 x 9.2 inches

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Reason To Buy

  • Details about ML
  • Level goes from low to high
  • Print color and clear layout


The book provides most of the knowledge related to ML. Through it, you can quickly learn to write complete ML with a simple and convenient method.

The layout and interior are explicitly presented and clearly, not creating much confusion for the reader.

It is suitable not only for beginners but also for those who want to improve their level. It goes from basic machine learning models like linear and logistic regression to advanced deep learning and general modeling.

Even as a beginner, I quickly finished this book without any difficulty.

Buying Guide


Data science is a field that consists of a lot of knowledge. If you are weak in a particular area, you should only buy books specializing in the subject you need.

In case you are a beginner, you can search or refer to content related to data science concepts and principles.


A book with a clear presentation and a logical layout are always better than a book without a precise sequence.

It helps you absorb knowledge, easy-to-read, easy-to-understand manner. To choose a book with a reasonable layout, you should consult the product description before bringing it home.


Each author has a different way of presenting content as well as form. Data science is highly academic, so we recommend you buy books by famous authors, doctorates or masters at universities.

Thus, the numbers and examples from the book will be more accurate and make you more reliable.


Can I self-study data science?

A degree is not a prerequisite for data science. It would be great if you had a great instructor with complete guidance in this area.

If you still need to get the above two factors, you can completely self-study through the knowledge online or the above books we have shared.

Is data science full of math?

Mathematics is an integral part of data science. Because whatever you are doing, including data analysis, charting, probability statistics, etc., everything is closely related to math and numbers. Mastering it is the foundation for you to delve into data science.

Can I learn data science in 2 months?

Honestly, you need more than two months to study data science. It just helps you to understand the definition or some other small details. You should learn more about it through books if you want to master this field.

Is Python enough for data science?

Python is a general-purpose programming language. You can use Python code for data analysis and web application development. Yet, to maintain and become good at data science besides python, you need to equip others with knowledge.

Is data science hard for beginners?

Data science is highly academic and can be difficult for beginners. If you love them enough and have a high self-study spirit, it will be easier than you think. We have recommended several types of data science books for beginners.


Data Science and Big Data Analytics is the best data science book to help you get an overview of the field. Besides, Head First Statistics is also a perfect choice if you want to dig into statistical analysis.

Books are always the best source of information and data for you to learn about data science. Hopefully, with the list shared above, you will choose for yourself some good books for self-study.