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.
Textual and contextual data analysis: A multivariate statistical approach using R
1st Edition
By Mónica Bécue-Bertaut, Ramón Alvarez-Esteban
July 22, 2026
Multidimensional statistical analysis of textual data is a powerful technique that enables researchers to uncover deeper insights into the context and meaning of documents. This book addresses the challenge of jointly analyzing textual and contextual data, presenting rigorous theoretical ...
Data Science in Healthcare: A Complete Guide
1st Edition
By Gayathri Delanerolle, Yassine Bouchareb, Konstantinos V. Katsikopoulos, Peter Phiri
May 28, 2026
This book brings together everything you need to know about data science within healthcare systems, with a primary focus on showing how to advance automated and non-automated analytical methods for extracting valuable insights from healthcare data. It draws upon a range of interconnected ...
What's the Question?: Deciding What You Really Want to Know
1st Edition
By David J. Hand
May 13, 2026
Statistics and data science aim to extract understanding from data and guide decision-making. However, before applying any analytical tools we need absolute clarity about what we want to know or accomplish. Ambiguous objectives inevitably lead to mistaken conclusions and flawed actions. This book ...
Test-Driven Data Analysis
1st Edition
By Nicholas J. Radcliffe
April 23, 2026
Test-driven data analysis is the synthesis of ideas from test-driven development of software to data-intensive work including data science, data analysis, and data engineering. It is a methodology for improving the quality of data and of analytical pipelines and processes. It can be thought of as ...
Deep-Learning-Assisted Statistical Methods with Examples in R
1st Edition
By Tianyu Zhan
March 17, 2026
This book explores how deep learning enhances statistical methods for hypothesis testing, point estimation, optimization, interpretation, and other aspects. It uniquely demonstrates leveraging deep learning to improve traditional statistical approaches, showcasing their superior performance in ...
Predictive Modelling for Football Analytics
1st Edition
By Leonardo Egidi, Dimitris Karlis, Ioannis Ntzoufras
November 06, 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 14, 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...