Data Science Books You Should Read in 2024

Looking for the best books to learn Data Science? Check out this list of top-rated Data Science books covering machine learning, data analysis, statistics, Python, R, and more. Whether you’re a beginner or an experienced Data Scientist, these books will help you enhance your skills and stay up-to-date with the latest trends.

If you are interested in learning data science, I am sure you must have been through many resources where you can either learn new skills or brush up on your existing ones. Books are always the best resources for learning as they contain much detailed content. This article will teach you about various data science books from the best authors. These data science books can clear your doubts and help you grasp the content most effectively.

To learn about data science, read our blog – What is data science?

Tips For Selecting The Best Data Science Books 

You should wisely choose the book you are going to buy. Don’t worry about the value of the book. Knowledge costs a lot. These tips can help to pick the best data science book to enhance your learning –

• Read all the reviews about the book before purchasing
• Know how to use an e-book
• Check out other recommended books online
• Do not judge a book by its cover
• There are free Kindle e-books, make use of the free knowledge

Best Data Science Books

The list of data science books contains sources of information on data science, machine learning, statistics, programming in R, Python, deep learning, etc. Let’s take a look.

Python Data Science Handbook: Developer Tools and Techniques – Essential Tools for Working with Data by Jake VanderPlas

This is hands down one of the best books you can get on Python for Data Science. The book includes updated step-by-step guides for using Jupyter, iPython, NumPy, Pandas, Scikit-Learn, Matplotlib, and other libraries and detailed explanations of the most used algorithms. Learning will be done through easily reproducible examples.

Top Review  

“This book has put thorough light on the libraries like NumPy, Pandas, and Metplotlib. Very useful for data science students, but this book is not for general Python or for those who want to learn Python from the beginning.”

To know more about the job profile and responsibilities of a Data Scientist, refer to our blog – What is Data Scientist?

R for Data Science: Import, Order, Transform, Visualize, and Model Data by Hadley Wickham and Garrett Grolemund

In this book, Garrett Grolemund will teach you what Data Science is, focusing on the R programming language and RStudio. It will not teach you statistics concepts from scratch but will focus on how to use the language so that you feel comfortable using utilities like ggplot2 or R Markdown. It covers very useful beginner tasks of manipulating and using databases, visualizing and exploring data, and pre-modelling phases.

Top Review

“The author has taken a good approach. Instead of giving you an introduction or much theory, the author teaches each concept with examples. You start with coding immediately, which is better. Exercise is also very good.”

Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas Muller

If you prefer a straightforward approach to Machine Learning for beginners with Python, this book is for you. You don’t need to know Python beforehand to understand the concepts. You will learn the most important machine learning algorithms and how and where to apply them. It is part of the typical workflow when working with data: preprocessing, evaluating, and implementing algorithms.

Explore Statistics for Data Science Online Courses

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems

In this book, Aurelien Geron explains the basic techniques of Machine learning, relying on tools and frameworks such as Scikit-learn, Keras, and Tensorflow. It does everything in a practical way without being overwhelmed with theory. It also covers advanced concepts like deep learning and neural networks. Exercises are included in each chapter to implement the examples and learn them easily.

Top Review

“A perfect book for ML Scikit and Tensorflow – This is one of the best books you can get for someone just starting out in ML, in its libraries, such as Tensorflow. It covers the basics very good. As a book, it is 5/5.”

Machine Learning with Python: Machine Learning and Deep Learning with Python, Scikit-learn, and TensorFlow 2 by Sebastian Raschka and Vahid Mirjalili

This book continues to teach you all the phases involved in Machine Learning processes. You will implement these algorithms using Python and its tools. A theoretical basis is necessary for understanding this book, which delves more deeply into the matter. Sometimes, you must make minor adjustments to the displayed code to accommodate the most up-to-date versions.

A Common–Sense Guide to Data Structures and Algorithms, 2e: Level Up Your Core Programming Skills, Jay Wengrow

This practical guide to data structures and algorithms goes beyond theory and significantly improves your programming skills. Learn how to use hash tables, trees, and graphs to improve code efficiency. The practical exercises in each chapter will help you practice what you have learned before moving on to the next topic. Algorithms and data structures are presented primarily as theoretical concepts, but this book focuses on learning these concepts to run your code faster and more efficiently.

Top Review

“The best book on DS and Algorithms I’ve ever read – This has undoubtedly been the best book I have read (and understood) about algorithms and DS. The author explains each topic in detail and does it without so much mathematical jargon”.

Smarter Data Science: Succeeding with Enterprise-Grade Data and AI Projects by Neal Fishman, Cole Stryker, and Grady Booch

In an enterprise environment, data science is often cornered and does not always feel its presence where it is needed most. Even the best and most skilled data scientists can’t extend their careers unless they can influence the rest of the organization. Smarter Data Science addresses these shortcomings by investigating why data science projects often fail at the enterprise level and how to fix them.

The book is designed for directors, managers, IT professionals, and analysts to effectively extend their data science programs to be predictable, reproducible, and ultimately beneficial to the entire organization. Learn how to create valuable data science initiatives and effectively engage everyone in your organization.

Top Review 

“They explain how to significantly increase the probability of success of what today is a competitive necessity for businesses, applying learnings and reusable techniques to “smarter” data science that can be extended to future data science use cases. This is a roadmap for arming the business user with faster, more precise, and insightful decision-making.”

Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, Second Edition by Peter Bruce, Andrew Bruce, and Peter Gedeck

This data science book helps today’s aspiring data scientists, who have not received formal training in statistics, master the basics. Practical statistics for data scientists go back to basics, but you can learn to apply statistical techniques to your daily work from a data science perspective. The recently released second edition provides examples of Python’s statistical applications, highlighting important (and unimportant) statistical concepts for data scientists to learn.

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

Big data never seems to get out of the news cycle. Data companies are gaining power, data breaches are taking place, personal and banking data are leaking, policy debates are rampant, and data privacy regulations are becoming law. This book discusses data’s effect on almost every aspect of their lives, from business to personal, even at the level of government and individual scientific disciplines.

Top Review

“Very interesting book. It is one of the books that I would recommend as a reference book, as it contains many examples and quotations.”

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The Kaggle Book covers the modelling strategies and general techniques for approaching tasks based on image, tabular, textual data, and reinforcement learning. You will learn to design better validation schemes and work more comfortably with different evaluation metrics.

Top Review

“One of the best Data Science books I have ever bought! The most appreciable feature is that it starts with the basics of all Data Science competitions, and even for a novice Data Science enthusiast, this book will serve a world of good.”

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps by Valliappa Lakshmanan

Machine Learning Design Patterns cover proven methods to help data scientists tackle common problems throughout the ML process. This book provides detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness.

Top Review

“There are many many books out there on Machine Learning detailing techniques, architectures, and frameworks but surprisingly this is the first of its kind to address common design patterns. Good ML design patterns hold their relevance over time much more than a framework or architecture might, so it’s surprising that this book stands alone in this topic.“

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Top Review

“The book not only covers the technical concepts but also covers tips for resume & portfolio building and how to reach out to hiring managers along with frameworks to ace behavioral questions.“

Conclusion

We hope we helped you make the right decision in picking the data science books based on your career aspirations and the skill sets you wish to develop. We will keep updating this blog so that you learn about the most updated versions of these books.

Keep learning!

Book Reviews and Reference Images – Amazon