Best Data Processing 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.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow is a must-read for anyone interested in machine learning. This book provides a comprehensive overview of the concepts and techniques required to build intelligent systems. The author, Aurelien Geron, uses Python frameworks such as scikit-learn, Keras, and TensorFlow to explain everything from simple linear regression to deep neural networks. The book includes numerous code examples and exercises that help readers apply what they've learned. The updated third edition covers new topics such as generative adversarial networks and deep reinforcement learning. Overall, this book is an excellent resource for anyone looking to learn machine learning from scratch or deepen their knowledge in the field.

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 Python Programming and SQL: 5 books in 1 is an all-in-one guide for beginners and advanced learners who want to master Python and SQL programming languages. The guide offers step-by-step instructions and practical experience, making it easy for readers to start coding in no time. It covers essential tools, strategies, and real-world applications with easy-to-understand examples and exercises. The book is an excellent resource for anyone looking to learn coding, from basic to advanced levels, and it provides excellent value for money as five books are bundled into one unique guide.

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.

Natural Language Processing with Transformers, Revised Edition is a practical guide for data scientists and coders interested in training and scaling large models using Hugging Face Transformers. The authors, including the creators of the library, provide a hands-on approach to integrating transformers in your applications and solving a variety of NLP tasks. The book covers topics such as cross-lingual transfer learning, distillation, pruning, and quantization. The revised edition is now in full color and offers updated content, making it a valuable resource for both beginners and experienced practitioners.

The book "Code: The Hidden Language of Computer Hardware and Software" is a classic guide that explains how computers work. Charles Petzold has updated this book with new chapters and interactive graphics to cater to this new age of computing. It is cleverly illustrated and easy to understand, making it a perfect fit for beginners in the field of computer science. The book delves deep into the bit-by-bit and gate-by-gate construction of every smart device's heart, the central processing unit, and how human ingenuity has shaped every electronic device we use. This book is highly recommended for anyone who wants to understand the mystery behind the ubiquitous computers that surround us.

This comprehensive guide, "Essential Math for AI", is a must-read for anyone interested in the AI field. It provides a comprehensive understanding of the underlying mathematics necessary to build successful AI systems, focusing on real-world applications and state-of-the-art models. The book is written in an immersive and conversational style, making it easy for anyone to learn and understand. It covers topics such as regression, neural networks, convolution, optimization, probability, graphs, random walks, Markov processes, differential equations, and more. The author has done an exceptional job of making math fun and engaging, making this book stand out from other math textbooks.

This book is a comprehensive history of data and its impact on our society, from the census enshrined in the US Constitution to the development of Google search. The authors, Chris Wiggins and Matthew L. Jones, explore how data has been used as a tool and weapon in shaping people, ideas, society, military operations, and economies. They also discuss new mathematical and computational techniques developed to contend with data. The book highlights the importance of understanding the trajectory of data to bend it to ends that we collectively choose. It's a must-read for those interested in the history of engineering and technology.