Best Data Science Books - Free and Paid - Editorial Recommendations for 2022

For the past year, we have looked at over 23,000 [1] data science books, and we have picked what we consider to be the best paid and free books in terms of technicality, ability to explain complex subjects, depth, and verified reviews.

Over the last decade, data science has become one of the most paid and highly reputed domains for professionals in the information technology field.

Nowadays, data science applications have become inevitable for most (if not all) businesses. Hence, there is a surge of proficient data science professionals.

Therefore, if you plan to move into this domain, you may find a wide variety of data-science-related books available online, which in turn, can be an arduous task to pick out the most notable books to get into data science.

This article aims to solve this conundrum by providing you with our editorial recommendations on the best and high-quality books for data science.

Disclosure: Our editorial team at Towards AI writes authentic and trustworthy reviews and may receive a small compensation on products we select to support Towards AI’s efforts. For this article, as an Amazon Associate Towards AI may receive a small commission from qualifying purchases made from it. For feedback, questions, or concerns, please email us pub@towardsai.net.

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1. Practical Statistics for Data Scientists:

Author(s): Peter Bruce, Andrew Bruce, Peter Gedeck

This book is ideal for absolute beginners. It covers a basic overview of all the prerequisite concepts to get deeper into the domain of data science. In this book, you will learn concepts of exploratory data analysis, random sampling, regression analysis, classification techniques, statistical machine learning methods, and much more. Other than theoretical concepts, it encompasses code examples in R as well as Python programming language. We find this a great resource to learn data science, as it’s all about getting you familiar with data science without diving into much depth. Other than that, you will also find additional resources that will lead you to understand some more advanced topics in data science. In conclusion, this is an excellent resource for data science beginners.

Grab a copy on Amazon.

2. Introduction to Machine Learning with Python:

Author(s): Andreas C. Muller, Sarah Guido

This book is an ideal option for those who want to kick start their journey in Data Science. With a friendly tone and illustrative examples, this book provides a clear explanation of fundamental concepts in data science and machine learning. The best thing about this book is that the reader does not require any prior knowledge of data science, machine learning, and Python. This book contains the — fundamental concepts and application of machine learning, advanced techniques for model evaluation, representation of data, the concept of the pipeline, suggestions for improving your data science and machine learning skills, and many more things. This book is probably one of the best for learning data science with Python.

Grab a copy on Amazon.

3. Business Data Science:

Author(s): Matt Taddy

This book by Matt Taddy, Ph.D. from Amazon Science focuses on the business perspective of data science. It covers topics that impact real business environments. It contains theory with appropriate coding exercises that help readers to gain useful insights from it. Applying our knowledge in the business domain can be challenging, as models, in theory, make different kinds of assumptions, and when they are applied in practice, sometimes we see surprising results than those presented on paper.

Taddy’s background and expertise in academia and industry make him the perfect author to write this book. We are confident that you will feel sure of applying your data science skills and knowledge in real-world scenarios after reading this book.

Grab a copy on Amazon.

4. Introduction to Probability:

Author(s): Joseph K. Blitzstein, Jessica Hwang

Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. Making it, perhaps, the best book to learn probabilities. This book is recommended for both beginners and experts as it starts with basic concepts and moves its way through the core concepts of probability that will help you build a solid foundation in the domain of data science. This book includes intuitive explanations, examples, diagrams, and practice problems. Each chapter of this book ends with its relevant code examples in R programming language. In the new edition, they have included online supplements that include interactive visualization and animations. The book has been one of the most popular books for about five decades, and that is one more reason why it should definitely be on your bookshelf.

Grab a copy on Amazon.

5. Data Science from Scratch:

Author(s): Joel Grus

In this book, you will learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have a strong aptitude for mathematics and some necessary programming skills, this book will help you get into the core of data science in a satisfying way. There are many books available online, which gives you the basic idea of the implementation of statistical models by using libraries. But after all, these libraries are made from scratch. So if you want to learn data science from scratch and enhance your knowledge in this domain, then this book will definitely help you achieve your goal. The topics of this book are — basics of statistics, cleaning and manipulating data, diving deep into fundamentals of machine learning algorithms, implementation of machine learning algorithms from scratch, exploration of natural language processing, recommender system, network analysis, and many more. So if you really want to study data science the hard way, this is the book for you.

Grab a copy on Amazon.

6. Naked Statistics:

Author(s): Charles Wheelan

This book gives us a lot of real-life examples of how statistical concepts apply in the real world. The tone of the book is witty and conversational. The author of this book does not go deep into the theories, but instead, he uses pretty compelling examples to help you understand even some of the complex statistical concepts. This book starts with fundamental concepts of statistics like a normal distribution, central limit theorem, and goes on to complex real-world problems and correlating data analysis and machine learning. All in all, if you are new to data science, this book will make you laugh while understanding statistical concepts.

Grab a copy on Amazon.

7. Python for Data Analysis:

Author(s): Wes McKinney

This book is another excellent read if you have some basic knowledge of data science concepts. This book covers almost every method for data analysis alongside the basics of python programming language. The book covers — use of Ipython shell and jupyter notebook for exploratory data analysis, basic and advanced features of NumPy, data analysis with pandas, how to get clean data, visualization with matplotlib, summarizing data with pandas, time series analysis, and many more. In short, we can say that the author gives you a complete idea of what you should expect by working as a data scientist. Apart from that, the book is comprehensive, easy to read, and self-paced.

Grab a copy on Amazon.

8. Hands-on Machine Learning with Scikit-Learn and TensorFlow:

Author(s): Aurélien Géron

This book is probably one of the largest in data science and machine learning, which is packed with fantastic knowledge. It is recommended for both beginners and experts to gain useful insights into this domain. This book has a little theory, but it has powerful examples supporting it, which makes it in this list. The topics included in this book are — neural networks, scikit-learn for machine learning projects, training models in machine learning, TensorFlow to build and train neural networks, and many more. We can confidently say that after going through this book, you will be able to dive deeper into deep learning and solve real-world problems.

Grab a copy on Amazon.

9. Head First Statistics:

Author(s): Dawn Griffiths

Just like the other books of headfirst, the tone of this book is amiable and conversational, so you will not get bored after reading a few pages. The book covers a range of topics covered in first-year statistics that are essential for data science. This book brings typically dry subjects to life by providing engaging and thought-provoking material full of visual-aids and real-life examples. In this book, you will start with topics of descriptive statistics — mean, median, mode, standard deviation, variance — and then move to the inferential statistics like correlation, regression, and others. It also includes a thorough explanation of normal, binomial, Poisson, geometric probability distributions. Other than that, this book is full of pictures and graphics that make statistics topics easy to understand. Overall it is a great book to brush up your concepts of statistics.

Grab a copy on Amazon.

10. Pattern Recognition and Machine Learning:

Author(s): Christopher M. Bishop

If you have already read a few books on Data Science and you are familiar with many machine learning algorithms, and you want to further improve your skills in this domain, then this is the book for you. This book dives deeper into machine learning algorithms and mathematics. The prerequisites for this book include familiarity with — linear and multivariate calculus, probability distributions, and a strong foundation of programming language. It is probably the best book to read if you are already familiar with machine learning and data science.

Grab a copy on Amazon.

11. Inflection Point:

Author(s): Scott Stawski

If you are bored with the technical content of data science and want to know how data science is actually used in real-life businesses, then this is the perfect book for you. This book takes a break from the technical point of view of data science and focuses on the business perspective of it. If you really want to get further into the domain of Data Science and want to know how all of these things bind together, then this is a must-read for you as it encompasses the author’s experiences that show how data science actually works in real life.

Grab a copy on Amazon.

Best Free Data Science Books:

1. Think Bayes:

Author(s): Allen B. Downey

Think Bayes is an introduction to Bayesian statistics using computational methods. If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you’ll learn how to solve statistical problems with Python code instead of mathematical notation and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become more transparent, and you’ll begin to apply these techniques to real-world problems.

Grab it for free on Green Tea Press.

2. Python for Data Science Handbook:

Author(s): Jake VanderPlas

If you are familiar with the basics of data science concepts, then this book is the best book to take your data science skills to the next notch. It includes a thorough explanation of python libraries for data analysis with code examples. Here is a few topics included in this book — utilizing Ipython and jupyter notebook in the best possible way, Numpy for efficient storage of data, pandas for manipulation and analysis of data, Matplotlib to visualize the data, scikit-learn to implement machine learning algorithms. In short, we can say that through this book, you will learn a lot about python libraries.

Grab it for free on GitHub.

Conclusion:

We hope you love reading these books and gain some useful insights on data science out of it. If you come across any phenomenal books on data science such as the ones mentioned in this list, please let us know by emailing us.

Thank you for reading!

References:

[1] Data from Amazon, https://www.amazon.com/s?k=data+science

[2] Green Tea Press, https://greenteapress.com/wp/

[3] Python Data Science Handbook, Github, https://github.com/jakevdp/PythonDataScienceHandbook

[4] Think Bayes, Green Tea Press, https://greenteapress.com/wp/think-bayes/