The Best Data Science Books to Read in 2021

Data science is a fast-growing and lucrative career path. Data science professionals play a considerable role in analyzing big data and using the findings to solve real-world problems. With enough knowledge and experience, you too could qualify for the best data science careers, which include statisticians, analysts, and computer scientists.

The Bureau of Labor Statistics indicates a 15 percent job growth rate for computer and information research scientists between 2019 and 2029. This growth rate is faster than any other profession, making the career appealing. To stake out a claim on one of these jobs, you can start your education with the top data science books surveyed below. 

What Is Data Science?

Data science involves the extraction of valuable information from large sets of structured and unstructured data. The data can come from the web, electronic devices, or customer research. This field combines statistics, artificial intelligence, programming, domain expertise, and data processing to derive actionable insights.

Data scientists will clean, aggregate, and manipulate raw data to facilitate data analysis. Once the results are in, the scientists review the data to discover meaningful patterns and trends. The final step is interpreting the information and presenting the client with a simplified version.

Why Is Data Science Important?

Data science breaks down large datasets, making it easier for organizations to create a smooth workflow. Business leaders can make informed decisions based on facts derived from data science applications. Organizations use data science processes to reduce manufacturing costs and enhance customer service through speedy delivery.

The real-world applications of data science are many. It has been applied in industries such as farming, risk management, fraud recognition, marketing, healthcare, and social media. Let’s take a look at a few real-world examples. 

Farmers rely on data science to determine productivity and decide on planting, irrigation, and harvesting. In healthcare, data scientists employ predictive analysis tools that can detect disease. In social media, data science enhances user experience by presenting personalized content through machine learning. 

In short, data science is a crucial player in improving the growth and well-being of people, products, and services. 

Best Data Science Books to Read Right Now

Data science books are the best learning resource for anyone looking to succeed in this field. There are hundreds of books written by industry experts to explain the ins and outs of data science. Below is an extensive list of the best data science books, organized by specialty.

Best Data Science Books for Beginners

An Introduction to Statistical Learning: with Applications in R 

An Introduction to Statistical Learning was written by four outstanding authors: Gareth James, Robert Tibshirani, Trevor Hastie, and Daniela Witten. It covers linear regression, clustering, resampling methods, and classification, offering insights on modeling and prediction techniques. 

You will get an overview of statistical concepts and the tools that help data scientists make sense of big data. It is perfect for beginners as well as professionals seeking to hone their skills. Each chapter has tutorials and case studies explained in R, which is an open source programming language that features a simple syntax for beginners.

Data Science from Scratch: First Principles with Python

Written by Joel Grus, this is a fantastic book for a complete beginner. You will understand the principles and master the tools that drive data science. Furthermore, you’ll get a crash course in Python, learn the basics of statistics and linear algebra, and explore the fundamentals of machine learning.

The book is affordably priced and is available on Kindle or in paperback. It also has an updated second edition for newer Python releases. Learn how to manipulate data and implement models, such as neural networks, decision trees, and clustering without data science experience.

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

This is a comprehensive book for beginners. You will learn how R transforms data into meaningful information that’s easy to read. The authors, Hadley Wickham and Garrett Grolemund, simplify the different applications of data science.

You will gain insight into various processes like importing, wrangling, exploring, and modeling data. Each chapter features an exercise that will help you gauge your understanding of every topic. R for Data Science is an Amazon bestseller, making it a widely trusted source of information.

Best Math and Statistics Books

Think Stats: Exploratory Data Analysis

This book by Allen B. Downey covers basic probability and concepts of statistics that are essential in data science. You will also learn how to perform statistical analysis computationally for programs written in Python. Therefore, before purchasing the book, ensure you have basic knowledge of Python.

The book is affordable and explores statistics extensively. The author is a computer science professor with years of experience in programming. Other titles by this author include Think Python, Think Complexity, and Think Bayes.

Introduction to Linear Algebra

Mathematics plays a pivotal role in data science. Therefore, learning critical math concepts like linear algebra is essential. This book by Gilbert Strang is a popular linear algebra textbook at the best schools for math and statistics. It also has several editions that take you through different aspects of linear algebra.

You will cover different topics, from engineering to economics, and analyze professional algorithms. The fifth edition also contains updated information on multivariable calculus, singular values, and matrix data analysis. You will also explore popular programming languages like MATLAB, Python, and Julia.

Introduction to Bayesian Statistics

This book is the third edition under a series of statistics books written by Wiliam Bolstad and James Curran. This is a perfect book for beginners and intermediary professionals seeking to understand Bayesian statistics. The third edition is broad and covers a wide variety of topics, from introduction to implementation.

By the end of the book, you’ll have insight into different methods, such as the Markov Chain Monte Carlo method. The book also features exercises that help you refresh your memory after every chapter.

Best Programming Books

Clean Code: A Handbook of Agile Software Craftsmanship

This book is the first edition of a 13-book series on programming by Robert C. Martin. The author divides the book into three parts. The parts highlight principles of writing code, case studies on cleaning code, and a list of solutions from the case studies.

You’ll learn the difference between good and bad code and how to create and format code. Readers also learn how to test and identify good and destructive codes. This book is perfect for programmers, developers, and software engineers seeking to understand coding.

Python Crash Course: A Hands-On Project-Based Introduction to Programming

This book by Eric Matthes is a best seller on Amazon, almost amassing a five-star rating. It covers the core concepts of Python, offering a crash course in the programming language. You will also explore code examples and guidance on how to code Python from scratch.

Python is a popular programming language applied in most, if not all, data science applications. Therefore, learning Python for data science will help you secure a lucrative job.  You will also gain programming skills that apply to other programming languages.

Cracking the Coding Interview: 189 Programming Questions and Solutions

The book features 189 questions regarding basic and complex programming algorithms. Gayle Laakman McDowell, the author, walks the reader through detailed steps that derive the solution. The steps help you understand how programming works and how to identify hints that offer quick solutions.

You will also learn five essential strategies that help you answer several programming questions while coding. Finally, you will explore how companies like Google hire developers and how to qualify. The book is insightful for beginners and experienced developers.

Best Machine Learning Books

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

This technical book covers a wide variety of machine learning concepts to kickstart your data science career. The book offers an introduction to Python machine learning, which is an essential part of data science. Aurelien Geron helps the reader understand different business intelligence systems and how to implement these systems.

Some of the highlights in the book include machine learning landscapes like neural networks and tracking projects using scikit-learn. You will gain a complete understanding of Keras, scikit-learn, and the TensorFlow library. 

The Hundred-Page Machine Learning Book

Written by Andriy Burkov, this book is the number one bestseller under artificial intelligence. It features high ratings and recommendations from leading data science experts globally. Karolis Urbonas, the Head of Data Science at Amazon, calls this book the best introduction to machine learning.

Generally, the book highlights everything you need to know in 100 pages. You will learn basic and core concepts of machine learning and how to handle machine learning models. The author is also an expert in building state-of-the-art machine learning systems.

Pattern Recognition and Machine Learning (Information Science and Statistics)

Christopher M. Bishop focuses on machine learning from the Bayesian angle. The author uses graphical models to explain probability distribution and explain solutions for different algorithms. It covers basic probability and other areas of inferential statistics, laying a firm foundation for data science.

However, you need basic knowledge of multivariable calculus and linear algebra before purchasing the book. You don’t need to be a professional data scientist to understand the book because the author uses simplified language.

Best Deep Learning Books 

Deep Learning (Adaptive Computation and Machine Learning Series)

Deep Learning is undoubtedly one of the best books on deep learning. Elon Musk, the CEO of Tesla and SpaceX, described it as a comprehensive book on deep learning. It defines and explains the relationship between deep learning and machine learning without mixing the two.

You will learn different deep learning concepts, such as numerical computation, information theory, and probability theory. Readers also analyze research information and explore practical applications. The book is perfect for beginners and professionals looking for an introductory book in this field.

Docker Deep Dive: Zero to Docker in a Single Book

Docker is crucial in deep learning. It is a platform that builds and manages data in machine learning applications. Nigel Poulton simplifies Docker in this book, allowing beginners and professionals to learn the basic concepts.

You’ll learn the Docker architecture, network analysis, security, and material for the Docker Certified Associate certification. Poulton also allows the reader to dive deep into core concepts of deep learning, such as containers and images.

Deep Learning with Python

Francois Chollet introduces deep learning to readers using Python and the Keras library. You’ll learn how to set up a deep learning environment, image modeling, and how to use deep learning for texts and sequences. 

The book is very detailed and features several exercises. Additionally, purchasing the hard copy gives you access to an eBook in ePub, PDF, and Kindle formats. 

Best Data Visualization Books

The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios

The book offers comprehensive details for developing successful dashboards. Steve Wexler, Jeffrey Shaffer, and Andy Cotgreave create different types of dashboards to help the reader understand data visualization. The dashboards are applicable across diverse industries like marketing, human resources, finance, healthcare, and sports.

There is also a section that focuses on practical applications and psychological factors you’ll encounter at work. By the end of the book, you will have the skills to design a personalized dashboard.

Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks

Jonathan Schwabish’s book aims to help readers create effective data visualizations. You will learn how to design unique graphs and charts. The author explains data visualization principles, the steps to follow when making style decisions, and what to do and what not to do when creating data visualizations.

There are more than 500 hands-on examples and surveys that explain different aspects of data visualization. You will also gain the skills to define your target audience and communicate your visuals effectively. The book serves both beginners and experts in the field.

Storytelling with Data: A Visual Guide for Business Professionals

Data visualization enhances data presentation by simplifying information. It involves storytelling, visual stimulation, and creating dashboards for the right audience. Cole Nussbaumer Knaflic teaches the readers the fundamentals of data visualization through storytelling.

You will gain insight on context and audiences, determining appropriate graphs and charts for every presentation, and cloud clutter suppression. This book will help you capture your audience’s attention by enhancing your design skills.

Final Thoughts

All of the books featured above are available on Amazon for Kindle and in hard copy. Keep in mind that the market is flooded with books that offer valuable insights into the vast field of data science. Therefore, you need to narrow down your specialty and figure out which data science concepts are most important to you before purchasing a book.