10 Free Data Science Books For 2025

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Entering the data science field presents an overwhelming abundance of resources, sometimes even too many. Not every resource is created equal, and many resources might not be perfect for your learning process.

To assist your data science learning journey, let's explore the top ten free data science books you should know about in 2025.

1. Veridical Data Science

 
Data science is a broad topic that encompasses everything from simple theory to advanced industry applications. The book Veridical Data Science by Bin Yu & Rebecca L. Barter presents the data science life cycle (DSLC), an essential subject if you want to execute a data science project.

The book itself is available for free in HTML format, but purchasing the hardcover will require an additional payment, which is expected since the topics covered are numerous and can be grouped into:

• Introduction to Veridical Data Science
• Preparing, Exploring, and Describing Data
• Prediction

If you need an introduction to data science, this book will be a good start.

2. Data Science: Theories, Models, Algorithms, and Analytics

 
When we want to break into a new field, we need to understand the foundational theory for what makes the field work. From the basic concepts to the applications, Data Science: Theories, Models, Algorithms, and Analytics by Sanjiv Ranjan Das will try to teach you what is necessary to become a data scientist.

There are many things you will learn from this book, including:

• Core Concepts in Data Science and Mathematics
• Programming
• Data Handling
• Visualization
• Statistical Modeling and Machine Learning
• Text Analytics
• Advanced Applications

It’s one of the most complete books that offers free learning about data science, so don’t miss it.

3. Think Python 3E

 
Python is one of the most common languages among data scientists and is often required for data science positions. Therefore, the Think Python book by Allen B. Downey is more important than ever for anyone who wants to break into data science.

Think Python is a book that introduces Python to anyone who has never programmed before or is struggling to learn it. The third edition adds more context and exercises with suggestions to help you learn more. Some subjects you will learn from this book include:

• Fundamentals of Programming
• Functions and Control Flow
• Data Structures and Algorithms
• Text Processing and Data Handling
• Object-Oriented Programming and Advanced Topics

Start with this book if you need an introduction to Python.

4. Python Data Science Handbook

 
Another fantastic free book on Python is the Python Data Science Handbook by Jake VanderPals. This book is perfect for those who want to learn about Data Science from a Python perspective, as it provides explanations and examples of execution.

The book is a bit older, but all the basics for understanding Python still work well. There are a few things you will learn through this book, including:

• Python and IPython
• NumPy
• Pandas
• Matplotlib Visualization
• Machine Learning

Read the book from start to finish for the best learning experience.

5. R for Data Science

 
Besides Python, R is the other programming language commonly used in statistical and data science work. It’s a versatile language used in many data analyses, so data scientists will benefit from R for Data Science by Hadley Wickham, Mine Cetinkaya-Rundel, and Garrett Grolemund.

The book will get you through with every basic foundation to use R for data science works, including:

• Programming with R
• Data Visualization
• Data Manipulation
• Data Transformation
• Reporting

This book will teach you the R Foundation and tremendously help your career.

6. Think Stats 3E

 
Statistics is the foundation for any data science work, meaning it’s a subject that every data scientist needs to understand. The Think Stats 3E book by Allen B. Downey is an excellent resource for building your statistical knowledge.

The third edition builds on the previous series, focusing on practical statistics in data science by teaching various techniques with execution examples, such as:

• Descriptive and Exploratory Analysis
• Probability and Distributions
• Statistical Relationships and Inference
• Modeling and Regression
• Advanced Analytical Techniques

This book will help enhance your statistical knowledge, so don’t miss it.

7. Statistics and Prediction Algorithms Through Case Studies

 
Like the previous book, statistics — especially the prediction algorithm — are essential tools for any data scientist. The book Statistics and Prediction Algorithms Through Case Studies by Rafael A Irizarry will teach you data analysis at the core using R, so you will understand all the concepts necessary for success with statistics.

Using this book you will go through many concepts, including:

• Summary Statistics
• Probability
• Statistical Inference
• Linear Models
• High Dimensional Data
• Machine Learning

This book will benefit you, even if you are not planning to use R for data science, as the concepts are universally applicable.

8. Probabilistic Programming & Bayesian Methods for Hackers

 
Statistics are vital for data science, and understanding statistics is essential for excelling in a data science job. One topic that data scientists must grasp is the probabilistic and Bayesian methods, which are helpful in the analysis process. Probabilistic Programming & Bayesian Methods for Hackers by Cameron Davidson-Pilon is a great resource in this regard.

The reader will learn much about probabilistic programming and the Bayesian methods in this book. The chapters include:

• Bayesian Method Introduction
• Using PyMC library
• Markov Chain Monte Carlo
• Law of Large Numbers
• Loss Function
• Prior Choices

If you need to understand more about Bayesian methods, this book is for you.

9. Think Bayes 2E

 
The Think Bayes book by Allen B. Downey is another fantastic book that covers the Bayesian method for data scientists. The book tries to adapt a more practical approach to learning Bayesian using Python code instead of mathematical notation.

The book offers an introduction to Bayesian to the real-world applications, which the book will explore the following topics:

• Bayesian Fundamentals and Probability
• Statistical Modeling and Estimation
• Decision-Making and Inference
• Advanced Bayesian Methods
• Practical Examples and Applications

Whether you are already familiar with Bayesian concepts or are just starting to learn, this book will provide an excellent resource in your pursuits.

10. Data Science at the Command Line

 
Modern data science isn’t limited to paper and pen; it now involves programming languages to accelerate the process. There are many things you can do with programming languages, and one of them is combining them with the command line system to make the data science process more efficient.

The command line system has been able to assist data scientists in their analytical work, and Data Science at the Command Line by Jeroen Jannsens will guide you through how to do it, covering all the essentials in its chapters:

• Data Acquisition and Input Handling
• Data Cleaning, Exploration, and Visualization
• Workflow and Tooling
• Scalable Computing and Pipeline Optimization
• Modeling and Integration with Analytical Tools

If you want to learn more about command line usage for data scientists, then this book is for you.

 
Navigating the world of data science isn’t easy, even for experienced professionals. That is why resources like free data science books can be a great addition to help us improve ourselves and accelerate our careers.

I hope this has helped!
 
 

Cornellius Yudha Wijaya is a data science assistant manager and data writer. While working full-time at Allianz Indonesia, he loves to share Python and data tips via social media and writing media. Cornellius writes on a variety of AI and machine learning topics.