We are surrounded by data, and the amount of new data available to us is growing every day. So is demand for skilled data professionals. When you’re just taking your first steps toward a career as a data analyst, it’s key to immerse yourself in the language, ideas, and trends of data. Books are one way to do that.
We’ve curated a list of data analysis books appropriate for beginners on a range of topics, from general overviews to topical selections on statistical programming languages, big data, and artificial intelligence. Add these books to your reading list to help you:
Assess whether a data analyst career would be a good fit for you
Familiarize yourself with the vocabulary and concepts of data analytics
Get job advice and prepare talking points for interviews
Stay atop the latest data trends
Learn new data analyst skills to launch or advance your career
Bookmark this page now so you can revisit it during your data analytics journey.
You’ll find no shortage of excellent books on data analytics out there, but we’ve decided to focus on those that are most relevant to beginners. Many of these titles offer an introduction or overview of a topic rather than a technical deep dive. Some of the more skills-based books include exercises to get you practicing real world data skills.
Best data analytics overview
The chapters in this book are organized much like an introductory college course—many universities have adopted it as their textbook. It’s an excellent introduction if you’re just getting started in data analytics or wondering what data analytics is all about. Besides high-level overviews of key data concepts, the book also includes:
Real-world examples of data analysis in practice
Case study exercises that could lead to potential portfolio pieces
Review questions to help you check your comprehension
R and Python data mining tutorials for complete beginners
While the book was originally published in 2014, it has been updated several times since (including in 2021) to cover increasingly important topics like data privacy, big data, artificial intelligence, and data science career advice.
Best data science overview
Reading this book provides a gentle immersion into the world of data science—perfect for someone coming from a non-technical background. The authors walk you through algorithms using clear language and visual explanations, so you don’t get bogged down in complex math.
While this book is geared toward beginners, it offers value to practicing data scientists as well. Use it as a refresher on how to communicate what you’re working on to business partners.
Best book to learn Python
If you’ve never written a line of code before (or if you still consider yourself a beginner), this book will have you writing your first program in minutes. Dr. Charles Severance of the University of Michigan walks readers through the process of learning to “speak” to a database through Python.
It’s a useful resource on its own and even more valuable when used alongside Dr. Chuck’s popular course, Python for Everybody (available on Coursera).
At the time of writing, you can download a free electronic version of Python for Everybody at py4e.com.
Best introduction to SQL
This is so much more than a book. When you buy this book on Structured Query Language (SQL), you get access to a sample database and SQL browser app, so you can put what you’re learning into action right away. You’ll also get lifetime access to a host of digital tools—workbooks and reference guides among them—to complement your learning.
This book covers topics like:
How to use SQL to communicate with relational databases
Key SQL queries to complete common data analysis tasks
Advice on how to pitch your new SQL skills to potential employers
Best big data book
Whether or not you’re involved in the world of data analytics, you’ve probably heard the term “big data” at some point. This book by two experts in the field goes beyond the buzzword to illuminate just how big data is already changing our world, for better and sometimes worse.
This isn’t a technical text to teach you big data algorithms. It’s more of a primer in what big data is, what it can do, and how it might impact the future.
Best business analytics book
This book digs deep into the importance of data for business decision making. If you’re interested in pursuing a career as a business analyst, consider this an introduction to how data science and business work together, and what goes into data-driven decision making.
The authors do a good job of outlining data science techniques and principles as they relate to business without getting caught up in the technical details of algorithms.
Honorable mention: Too Big to Ignore: The Business Case for Big Data by Phil Simon
Best artificial intelligence book
By reading this book, you can start to separate the hype surrounding the idea of artificial intelligence (AI) from the reality. Author Melanie Mitchell, a computer scientist, explores the history of AI and the people behind it to help readers better understand complex concepts like neural networks, natural language processing, and computer vision models.
While data analysts don’t necessarily need a deep understanding of AI, it can be helpful to understand these technologies and their impact on the world of data analytics. Mitchell approaches these topics in a way that’s clear and engaging.
Best data visualization book
In data analysis, our data is often only as good as the stories we tell with it. This book walks you through the fundamentals of communicating with data through storytelling and visualization. It combines theory with real-world examples to help you:
Choose the right visualization for the right situation
Eliminate clutter and highlight the most important parts of the data
Think like a visual designer
Build presentations using multiple visuals to tell a compelling story
Reading this book won’t teach you to create masterful visualizations using R or Tableau, but its insights can equip you to use those tools more effectively when you do learn them.
Best machine learning book
This title delivers on its promise: an overview of machine learning in a little bit more than 100 pages (140 to be exact). It’s short enough to read in a single sitting. Andriy Burkov offers a solid introduction to the field, even if you have no statistical or programming experience.
This compact read covers an immense amount of information. Topics include supervised and unsupervised learning, neural networks, cluster analysis, and hyperparameter tuning. If you’re not familiar with those terms, don’t worry. You will be after reading this one. You can always turn to the companion wiki for recommendations on further reading and resources.
Best business intelligence book
This book explores how the trinity of people, process, and information come together to drive business success in the modern world. This is not a book about traditional business intelligence (BI) concepts. Instead, it outlines the ways in which BI can fall short and presents new models and frameworks to improve the practice.
If you’re looking for an overview of the past, present, and future of BI, give this book a try. Topics discussed include:
The birth of the biz-tech ecosystem
Practical tips for using big data
Data-based, intuitive, and collaborative decision making (and why companies need all three)
Best statistics book
If you need a refresher of what you learned in college statistics, pick up this book. If you’re someone who struggles with mathematical concepts presented as a series of numbers and symbols stripped of context, pick up this book.
Charles Wheelan dives into key concepts in statistical analysis—correlation, regression, and inference—in a way that’s both enlightening and entertaining. Wheelan makes a good (and humorous) case for why everyone should understand statistics in our modern world, not just data professionals.
You may not walk away knowing with mastery of statistics. But this book can help you understand the underlying concepts and why they matter, making it an excellent companion to more technical statistical coursework.
Best book on data bias
Big data can be a powerful tool, and this book serves as a warning and reminder that we need to use it responsibly. Data scientist and mathematician Cathy O'Neil explores the consequences of machines making decisions about our lives, and how the algorithms driving those decisions often reinforce discrimination.
Even if you don’t agree with the author on every point, you might walk away with a better understanding of the darker side of data. These relevant and urgent insights are particularly important for those just getting started in the world of data—those whose responsibility it will be to ensure that the data of the future is used for the benefit of all, not just the privileged.
Honorable mention: Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Umoja Noble
If you’re interested in data analysis and ready to take the next step toward a career in the field, get started for free in one of the many Professional Certificates available on Coursera.
Learn what a data analyst does and get an introduction to R programming with the Google Data Analytics Professional Certificate.
Explore various roles in the world of data while learning Python with the IBM Data Analyst Professional Certificate.
Learn the entire end-to-end data analyst workflow with the IBM Data Analyst with Excel and R Professional Certificate.