Chapman & Hall/CRC Data Science Series
About the Book Series
Reflecting the interdisciplinary nature of the field, this new book series brings together researchers, practitioners, and instructors from statistics, computer science, machine learning, and analytics. The series will publish cutting-edge research, industry applications, and textbooks in data science.
Features:
- Presents the latest research and applications in the field, including new statistical and computational techniques
- Covers a broad range of interdisciplinary topics
- Provides guidance on the use of software for data science, including R, Python, and Julia
- Includes both introductory and advanced material for students and professionals
- Presents concepts while assuming minimal theoretical background
The scope of the series is broad, including titles in machine learning, pattern recognition, predictive analytics, business analytics, visualization, programming, software, learning analytics, data collection and wrangling, interactive graphics, reproducible research, and more. The inclusion of examples, applications, and code implementation is essential.
Please Contact Us if you have an idea for a book for the series.
Predictive Modelling for Football Analytics
1st Edition
By Leonardo Egidi, Dimitris Karlis, Ioannis Ntzoufras
November 07, 2025
Predictive Modelling for Football Analytics discusses the most well-known models and the main computational tools for the football analytics domain. It further introduces the footBayes R package that accompanies the reader through all the examples proposed in the book. It aims to be both a ...
Models Demystified: A Practical Guide from Linear Regression to Deep Learning
1st Edition
By Michael Clark, Seth Berry
August 15, 2025
Unlock the Power of Data Science and Machine Learning In this comprehensive guide, we delve into the world of data science, machinelearning, and AI modeling, providing readers with a robust foundation and practical skills to tackle real-world problems. From basic modeling techniques to advanced ...
Natural Language Processing in the Real World: Text Processing, Analytics, and Classification
1st Edition
By Jyotika Singh
May 05, 2025
Natural Language Processing in the Real World is a practical guide for applying data science and machine learning to build Natural Language Processing (NLP) solutions. Where traditional, academic-taught NLP is often accompanied by a data source or dataset to aid solution building, this book is ...
Introduction to Classifier Performance Analysis with R
1st Edition
By Sutaip L.C. Saw
December 03, 2024
Classification problems are common in business, medicine, science, engineering and other sectors of the economy. Data scientists and machine learning professionals solve these problems through the use of classifiers. Choosing one of these data driven classification algorithms for a given problem is...
Mathematical Engineering of Deep Learning
1st Edition
By Benoit Liquet, Sarat Moka, Yoni Nazarathy
October 03, 2024
Mathematical Engineering of Deep Learning provides a complete and concise overview of deep learning using the language of mathematics. The book provides a self-contained background on machine learning and optimization algorithms and progresses through the key ideas of deep learning. These ideas and...
Getting (more out of) Graphics: Practice and Principles of Data Visualisation
1st Edition
By Antony Unwin
September 13, 2024
Data graphics are used extensively to present information. Understanding graphics is a lot about understanding the data represented by the graphics, having a feel not just for the numbers themselves, the reliability and uncertainty associated with them, but also for what they mean. This book ...
Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics, without the Hype
1st Edition
By Douglas Gray, Evan Shellshear
September 05, 2024
The field of artificial intelligence, data science, and analytics is crippling itself. Exaggerated promises of unrealistic technologies, simplifications of complex projects, and marketing hype are leading to an erosion of trust in one of our most critical approaches to making decisions: data driven...
Data Science: A First Introduction with Python
1st Edition
By Tiffany Timbers, Trevor Campbell, Melissa Lee, Joel Ostblom, Lindsey Heagy
August 23, 2024
Data Science: A First Introduction with Python focuses on using the Python programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. It emphasizes ...
Introduction to Data Science: Data Wrangling and Visualization with R
2nd Edition
By Rafael A. Irizarry
August 02, 2024
Unlike the first edition, the new edition has been split into two books. Thoroughly revised and updated, this is the first book of the second edition of Introduction to Data Science: Data Wrangling and Visualization with R. It introduces skills that can help you tackle real-world data analysis ...
DevOps for Data Science
1st Edition
By Alex Gold
June 19, 2024
Data Scientists are experts at analyzing, modelling and visualizing data but, at one point or another, have all encountered difficulties in collaborating with or delivering their work to the people and systems that matter. Born out of the agile software movement, DevOps is a set of practices, ...
The Data Preparation Journey: Finding Your Way with R
1st Edition
By Martin Hugh Monkman
May 07, 2024
The Data Preparation Journey: Finding Your Way With R introduces the principles of data preparation within in a systematic approach that follows a typical data science or statistical workflow. With that context, readers will work through practical solutions to resolving problems in data using the ...
Research Software Engineering: A Guide to the Open Source Ecosystem
1st Edition
By Matthias Bannert
April 17, 2024
Research Software Engineering: A Guide to the Open Source Ecosystem strives to give a big-picture overview and an understanding of the opportunities of programming as an approach to analytics and statistics. The book argues that a solid "programming" skill level is not only well within reach for ...