Stay up to date with the languages, trends, and ideas permeating the field of data analytics, and add these data analytics books to your reading list.
We can find data all around us, and the amount of new data available grows daily. So is the demand for skilled data professionals. When taking your first steps towards a career as a data analyst, books can help you learn the terms, key ideas, and concepts popular in the field.
We’ve curated a list of data analysis books for beginners on various topics, from general overviews to topical selections on statistical programming languages, big data, and artificial intelligence (AI). Add these books to your reading list to help you:
Assess whether a data analyst career would be a good fit for you
Familiarise 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 to revisit it during your data analytics journey.
You’ll find no shortage of excellent books on data analytics, but we’ve decided to focus on the most relevant for beginners. Many of these titles offer an introduction or overview of a topic rather than a technical deep dive. Some more skills-based books include exercises to get you practising real-world data skills.
Best data analytics overview
The chapters in this book are organised 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 it 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
Whilst initially published in 2014, the book has gone through several updates (including in 2022) to cover increasingly essential 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 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.
The book offers value to practising data scientists, while it is geared toward beginners. Use it as a refresher on communicating your work to business partners.
Best book to learn Python
If you’ve never written a line of code before (or still consider yourself a beginner), this book will have you writing your first program in minutes. Dr. Charles Severance of the University of Michigan in the United States walks readers through learning to “speak” to a database through Python.
It’s a helpful resource on its own and even more valuable when used alongside Dr. Severance's popular course, Python for Everybody (available on Coursera).
The 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 to put what you’re learning into action immediately. You’ll also get lifetime access to various digital tools—workbooks and reference guides—to complement your learning.
This book covers topics like:
Database structures
How to use SQL to communicate with relational databases
Key SQL queries to complete common data analysis tasks
Advice on how to promote your new SQL skills to potential employers
Read more: 4 SQL Certifications for Your Data Career in 2023
Best big data book
Whether or not you’re involved in 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 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 on what big data is, what it can do, and how it might impact the future.
Read more: What Is Big Data Analytics? Definition, Benefits, and More
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 an excellent job outlining data science techniques and principles as they relate to business without getting caught up in the technical details of algorithms.
Honourable 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 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.
Whilst 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 clearly and engagingly.
Best data visualisation 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 visualisation. It combines theory with real-world examples to help you:
Recognise context
Choose the correct visualisation for the right situation
Eliminate clutter and highlight the most essential 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 visualisations 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 about 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, and cluster analysis. If you’re unfamiliar with those terms, don’t worry—you will be after reading this book. 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, processes, 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 how BI can fall short and presents new models and frameworks to improve the practice.
If you’re looking for an overview of BI's past, present, and future, 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
Pick up this book if you need a refresher on what you learnt in college statistics. Pick up this book if you struggle with mathematical concepts presented as a series of numbers and symbols stripped of context.
Charles Wheelan dives into key concepts in statistical analysis—correlation, regression, and inference—in an enlightening and entertaining way. Wheelan makes a good (and humourous) case for why everyone should understand statistics in our modern world, not just data professionals.
You may not walk away with a mastery of statistics. Still, 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 must 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 disagree with the author, you might walk away with a better understanding of the darker side of data. These relevant and urgent insights are crucial for those just getting started in the world of data—those who may be tasked with ensuring that future data is used for the benefit of all, not just the privileged.
Honourable 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 towards a career in the field, get started for free with the Google Data Analytics Professional Certificate. Learn what a data analyst does and get an introduction to R programming in as little as six months. Skills you can gain through this programme include data visualisation, data collection, and data ethics.
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