21 Best Data Science Books for Beginners

Data science is thriving, with technologies like machine learning, algorithms, and predictive models driving everyday insights and business decisions. All of this can be tough to grasp if you're just starting out. Luckily, there are many excellent books about data science covering various topics in the field. Here, I've compiled a list of the best books that break down the essentials and guide you through the fascinating world of data science.

Technical Data Science Books for Beginners

Data science is a mix of three different disciplines. One is programming and computer science; the second is linear algebra, statistics, and math-heavy analytics; and the third is machine learning algorithms. If all this sounds confusing, a good way to start is to learn the key concepts because you'll need them in your data science career.

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

This data science handbook is an perfect resource for beginners eager to learn the data science fundamentals from the ground up. The book introduces essential concepts and tools using Python, one of the most popular programming languages in the data community.

You'll cover topics like linear algebra, statistics, and probability, applying them all using Python. You'll also master the fundamentals of data analytics and machine learning by implementing linear regression, logistic regression, decision trees, and neural networks. Plus, you'll get an introduction to the recommender system, natural language processing, and network analysis.

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

Statistics form the backbone of data science, but not every data scientist is familiar with statistical learning. This book is your perfect guide to data science using the Python and R programming languages. It offers best practices and examples to help you apply statistical methods and avoid misusing them. 

Introduction to Linear Algebra by Gilbert Strang

Linear algebra is a field of mathematics essential to a deeper understanding of machine learning. It studies lines and planes, vector spaces, and mapping required for linear transforms. If you want to learn linear algebra, you need to check out this book and the great additional resources it provides.

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

This guidebook helps you implement machine learning and deep learning algorithms using Scikit-Learn, Tensorflow, and Keras. The book covers training models like support vector machines, decision trees, random forests, neural networks, and ensemble methods.

Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

This essential read offers practical examples and implementations of deep learning algorithms. It covers deep learning research, practical frameworks, and applied math and machine learning fundamentals. The best thing about this book is that it's designed for beginners – and it's one of the rare books available online, free of charge.

Deep Learning for Coders with FastAI & PyTorch by Jeremy Howard and Sylvain Gugger

Looking for a technical book on deep learning? This hands-on guide is for you. Learn to implement deep learning algorithms from scratch using PyTorch in computer vision, natural language processing, and tabular data. The authors also teach you how to improve accuracy, speed, and reliability, and turn your models into web applications.

Non-Technical Books for Data Science Professionals

If you're a data science field newbie, you'll want to understand how to approach any data science problem before diving into code. Avoid common pitfalls and get a solid foundation in statistics, probability, data visualization, and more with these non-technical books.

Weapons of Math Destruction by Cathy O'Neil

How would you feel being fired based on a random number generator? How about not getting hired due to illegal questions about your mental health? Mathematician Cathy O'Neil reveals the risks of using massive data sets and data science tools without understanding their impact. She shows how this can lead to decisions that worsen inequality and harm vulnerable individuals.

Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are by Seth Stephens-Davidowitz

Do parents treat their sons differently from their daughters? Can you game the stock market? How many people actually read the books they buy? The book focuses on the power of data and the premise that we can use data to learn what people really think, want, and do.

Naked Statistics: Stripping the Dread from the Data by Charles Wheelan

Ever wonder how Netflix predicts your favorite movies or what's driving the rise in autism rates? Read this book to learn how the right data and well-chosen statistical tools can provide answers. It's perfect for anyone looking to learn data science, especially if it's been a while since your last math course.

Storytelling with Data by Cole Nussbaumer Knaflic

This insightful book discusses the art of data visualization. Ideal for those interested in data science for business, it teaches how to present data effectively by understanding and communicating it beyond conventional tools. Emphasizing context, audience, and storytelling, the book also provides real-world examples to elevate your presentations.

The Signal and the Noise: Why So Many Predictions Fail – but Some Don't by Nate Silver

Why do many of the predictions people make fail to happen? Nate Silver argues that it's often due to confusing probability with certainty: we often mistake more confident predictions for more accurate ones. This book will help you enhance your data literacy and tell true signals from noise in data analysis.

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Humans dream of superintelligent machines, but how smart are today's AI programs, really? This book makes a clear separation between science fiction and AI achievements, offering an honest look at the current state of AI and the exciting challenges that lie ahead.

Books for Data Scientists on Developing Soft Skills

We recently highlighted how soft skills are just as essential as technical skills for a successful career in data science. The good news is that you can improve your soft skills by reading books. What follows is a selection of somewhat unusual reads that are definitely worthy of your attention.

Infinite Jest by David Foster Wallace

This is probably the hippest book out there. Many people claim to have read it, but few actually completed it. David Foster Wallace gave his all when writing Infinite Jest. While reading, you'll improve your patience and attention to detail and be able to savor storytelling at its best. This book offers to stretch your thinking and turn reading into meditation. We recommend watching this video as well!

Waking Up by Sam Harris

Talking of meditation, I often raised my eyebrows when my meditation teacher talked about "connecting with the universe" and "embracing my inner energy." As an atheist, I struggled to find meditation techniques that did not have either a religious or a pseudoscientific element (such as "karma"). Then I discovered Sam Harris's Waking Up app and his book Waking Up, exploring spirituality without religion. This accessible book redefines what "spirituality" can mean.

Why We Sleep by Matthew Walker

Matthew Walker's Why We Sleep discusses the importance of living a balanced life. Despite some debate over his claims, Lithuanian neuroscientist Dr. Laura Bojarskaitė assured me that the book is a valuable read for anyone wanting to understand sleep better.

Thinking, Fast and Slow by Daniel Kahneman
Behave: The Biology of Humans at Our Best and Worst by Robert Sapolsky
The Idiot Brain by Dean Burnett
The Brain That Changes Itself by Norman Doidge

To make better decisions, we need to understand how our brain works. Start with Thinking, Fast and Slow by Daniel Kahneman. It's a challenging read but invaluable for understanding your behavior patterns. After that, explore easier books like Behave: The Biology of Humans at Our Best and Worst by Robert Sapolsky, The Idiot Brain by Dean Burnett, and The Brain That Changes Itself by Norman Doidge. These will give you good insights into our decision-making processes and capabilities.

Demon Haunted World by Dr. Carl Sagan
The Skeptics' Guide To the Universe by Dr. Steven Novella, Bob Novella, Cara Santa Maria, Jay Novella, Evan Bernstein

In a world full of fakes, how can we tell what's real ? It's challenging but doable. Start with Demon Haunted World by the legendary astronomer Carl Sagan. While some aspects are dated, the wisdom and rational thinking make this book timeless. The Skeptics' Guide To the Universe complements it well – it's a great book to help you recognize biases and logical fallacies, enhancing your critical thinking.

Feynman by Jim Ottaviani, Leland Myrick

Richard Feynman played a role in one of the greatest and most terrible creations of humankind – the atomic bomb. None of this was lost on Feynman, and he only reluctantly accepted the Nobel Prize awarded to him in 1965. What he excelled at was teaching and inspiring curiosity. There's a reason why his textbooks are still bestsellers decades after they were first published. Jim Ottaviani and Leland Myrick's graphic novel offers a captivating glimpse into Feynman's world in one evening's read.

Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World by Dr. Bruce Schneier

For data scientists and anyone online, Dr. Bruce Schneier's Data and Goliath is esential. It dives into the moral complexities of data collection, prompting us to think about how our data is used. This is crucial reading for understanding the ethical landscape of the digital age.

Learn Data Science on Your Own Terms

The journey into data science can be challenging but hugely rewarding with the right resources. From mastering programming and statistical methods to exploring the ethical implications of data use, the listed books will give you a solid foundation.

If you're looking for a more structured approach and personalized feedback from experts, take a look at Turing College's Data Science program. It focuses on practical, real-world applications, preparing you for a successful career from the day one. 

Whether you choose self-study or an online program, becoming a data scientist is a path filled with opportunities to learn, grow, and innovate. 

Author: Goda Raibyte