Best Data Mining Books of 2025

Fundamentals of Data Engineering: Plan and Build Robust Data Systems is a practical guide for software engineers, data scientists, and analysts seeking a comprehensive view of data engineering. The authors, Joe Reis and Matt Housley, walk readers through the data engineering lifecycle, providing an end-to-end framework of best practices for designing and building a robust architecture. Readers will also learn how to evaluate the best technologies available and incorporate data governance and security across the data engineering lifecycle. This book is a valuable resource for anyone looking to navigate the rapidly-growing field of data engineering.

Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street is a must-read guide for anyone who wants to land their dream job in data science, data analysis, or machine learning. Authored by two ex-Facebook employees, this 301-page book offers comprehensive coverage of the most frequently tested topics in data interviews. It provides detailed step-by-step solutions to 201 real data science interview questions asked by top companies, including Facebook, Google, Amazon, Netflix, Two Sigma, and Citadel. Additionally, the book offers valuable career advice on crafting your resume, creating portfolio projects, networking, and more. Overall, Ace the Data Science Interview is an invaluable resource for anyone looking to break into the data science industry.

The third edition of "Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter" by Wes McKinney is an essential guide for anyone looking to manipulate, process, clean, and crunch datasets in Python. With practical case studies and the latest versions of pandas, NumPy, and Jupyter, this book is perfect for those new to Python and data science. The author, the creator of the Python pandas project, provides readers with thorough, detailed examples to solve real-world data analysis problems. Overall, this book is a must-have for anyone looking to improve their data analysis skills in Python.

The second edition of "Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python" is a comprehensive guide that provides practical guidance on applying statistical methods to data science. The book covers key statistical techniques, including exploratory data analysis, regression, and classification, and teaches readers how to avoid common statistical mistakes. The authors, Peter Bruce and Andrew Bruce, have extensive experience in statistics and data science, and the book is written in an accessible, readable format. This book is a must-read for data scientists who want to improve their statistical knowledge and apply it to real-world problems.

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition is a comprehensive guide to statistical learning that covers a broad range of topics, from supervised learning to unsupervised learning. The book is well-organized and includes many examples, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The second edition has been updated to include new material on graphical models, random forests, ensemble methods, and more. Overall, this book is highly recommended for anyone looking to deepen their understanding of statistical learning.

Becoming a Data Head by Alex Gutman and Jordan Goldmeier is a comprehensive guide for anyone looking to become an active participant in data science, statistics, and machine learning. The authors make this complex space simple by breaking down the 'data process' into understandable patterns. The book offers a language and tools necessary to talk and think critically about data science, covering everything from the personalities you’ll work with to the math behind the algorithms. It is a must-read for business professionals, engineers, executives, and aspiring data scientists who want to become more valuable employees and make their organization more successful.

Learning SQL: Generate, Manipulate, and Retrieve Data is an essential guide for developers seeking to master SQL fundamentals. With comprehensive coverage of SQL basics and advanced features, the latest edition includes new chapters on SQL and big data, analytic functions, and working with large databases. The author, Alan Beaulieu, delivers self-contained lessons on key SQL concepts and techniques with numerous illustrations and annotated examples. Exercises ensure readers can practice the skills they learn. This book is a must-read for anyone looking to interact with data and put the power and flexibility of SQL to work.

This book, written by experimentation experts at Google, LinkedIn, and Microsoft, is a practical guide for those looking to accelerate innovation through trustworthy online controlled experiments, or A/B tests. The authors share their experiences, pitfalls, and advice for students and industry professionals getting started with experiments. The book is a great resource for executives, leaders, researchers, or engineers looking to use online controlled experiments. This is a rigorous yet accessible book that has lots of practical real-world examples and lessons learned over many years of the application of these techniques at scale.

Data Science for Business is a comprehensive guide to understanding data science and its role in business decision-making. Written by experts Foster Provost and Tom Fawcett, this book teaches readers the principles of data-analytic thinking and the various data-mining techniques used today. It includes real-world business examples to illustrate these principles and helps improve communication between business stakeholders and data scientists. The book also provides guidance on how to participate intelligently in data science projects and how to apply data science principles when interviewing data science job candidates. Overall, it is an essential resource for anyone looking to gain a competitive advantage through the use of data.

"SQL for Data Analysis: Advanced Techniques for Transforming Data into Insights" is a practical book that provides new and hidden ways to improve your SQL skills, solve problems, and make the most of SQL as part of your workflow. It covers both common and exotic SQL functions such as joins, window functions, subqueries, and regular expressions in new, innovative ways. The author, Cathy Tanimura, has over 20 years of experience analyzing data with SQL across most of the major proprietary and open source databases. This book is a must-have reference for anyone who works with SQL databases.