12 Data Analytics Books for Beginners: A 2023 Reading List

Written by Coursera • Updated on

Immerse yourself in the language, ideas, and trends of data with this 2023 data analyst reading list.

An older woman sits outside at a wooden table reading a book on her tablet device.

We are surrounded by data, and the amount of new data available to us is growing every day. So is the 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.

Books about data analytics for beginners

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.

1. Data Analytics Made Accessible by Dr. Anil Maheshwari

Best data analytics overview

The chapters in this book are organized much like an introductory college course— in fact, 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 2022) to cover increasingly important topics like data privacy, big data, artificial intelligence, and data science career advice.

2. Numsense! Data Science for the Layman: No Math Added by Annalyn Ng and Kenneth Soo

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 also offers value to practicing data scientists. Use it as a refresher on communicating what you’re working on to business partners.

3. Python for Everybody: Exploring Data in Python 3 by Dr. Charles Russell Severance

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 write 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.

Read more: How Long Does it Take to Learn Python? (+ Tips for Learning) 

It’s a useful resource on its own and even more valuable when used alongside Dr. Severance's popular course, Python for Everybody (available on Coursera). 

TIP: At the time of this writing, you can download a free electronic version of Python for Everybody at py4e.com.



Python for Everybody

Learn to Program and Analyze Data with Python. Develop programs to gather, clean, analyze, and visualize data.


(201,562 ratings)

1,433,332 already enrolled


Average time: 8 month(s)

Learn at your own pace

Skills you'll build:

Json, Xml, Python Programming, Database (DBMS), Python Syntax And Semantics, Basic Programming Language, Computer Programming, Data Structure, Tuple, Web Scraping, Sqlite, SQL, Data Analysis, Data Visualization (DataViz)

4. SQL QuickStart Guide: The Simplified Beginner's Guide to Managing, Analyzing, and Manipulating Data With SQL by Walter Shields

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 immediately put what you’re learning into action. 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:

  • Database structures

  • 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

Read more: 5 SQL Certifications for Your Data Career

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

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.

Read more: What Is Big Data? A Layperson's Guide

6. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett

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

7. Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

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.

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.

Read more: What Is an AI Engineer? (And How to Become One)

8. Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic 

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:

  • Recognize context

  • 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. 

9. The Hundred-Page Machine Learning Book by Andriy Burkov

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.


10. Business unIntelligence: Insight and Innovation beyond Analytics and Big Data by Dr. Barry Devlin

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 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)

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

Best statistics book

If you need a refresher of what you learned in college statistics, pick up this book. If you struggle 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. 

12. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil

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

Get started in data analytics

If you’re interested in data analysis and ready to take the next step toward a career in the field, get started for free with 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. Or, learn the entire end-to-end data analyst workflow with the IBM Data Analyst with Excel and R Professional Certificate.

Whatever your skill level, Coursera has something for you.


professional certificate

Google Data Analytics

This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required.


(104,661 ratings)

1,473,792 already enrolled


Average time: 6 month(s)

Learn at your own pace

Skills you'll build:

Spreadsheet, Data Cleansing, Data Analysis, Data Visualization (DataViz), SQL, Questioning, Decision-Making, Problem Solving, Metadata, Data Collection, Data Ethics, Sample Size Determination, Data Integrity, Data Calculations, Data Aggregation, Tableau Software, Presentation, R Programming, R Markdown, Rstudio, Job portfolio, case study

Written by Coursera • Updated on

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

Develop career skills and credentials to stand out

  • Build in demand career skills with experts from leading companies and universities
  • Choose from over 8000 courses, hands-on projects, and certificate programs
  • Learn on your terms with flexible schedules and on-demand courses