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.
Data Science and Analytics Strategy: An Emergent Design Approach
1st Edition
By Kailash Awati, Alexander Scriven
April 05, 2023
This book describes how to establish data science and analytics capabilities in organisations using Emergent Design, an evolutionary approach that increases the chances of successful outcomes while minimising upfront investment. Based on their experiences and those of a number of data leaders, the ...
Introduction to Environmental Data Science
1st Edition
By Jerry Davis
March 13, 2023
Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models; ...
How to Think about Data Science
1st Edition
By Diego Miranda-Saavedra
December 23, 2022
This book is a timely and critical introduction for those interested in what data science is (and isn’t), and how it should be applied. The language is conversational and the content is accessible for readers without a quantitative or computational background; but, at the same time, it is also a ...
Urban Informatics: Using Big Data to Understand and Serve Communities
1st Edition
By Daniel T. O'Brien
December 08, 2022
Urban Informatics: Using Big Data to Understand and Serve Communities introduces the reader to the tools of data management, analysis, and manipulation using R statistical software. Designed for undergraduate and above level courses, this book is an ideal onramp for the study of urban informatics ...
Data Science for Infectious Disease Data Analytics: An Introduction with R
1st Edition
By Lily Wang
December 05, 2022
Data Science for Infectious Disease Data Analytics: An Introduction with R provides an overview of modern data science tools and methods that have been developed specifically to analyze infectious disease data. With a quick start guide to epidemiological data visualization and analysis in R, this ...
Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models
1st Edition
By Przemyslaw Biecek, Tomasz Burzykowski
September 26, 2022
Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack ...
Cybersecurity Analytics
1st Edition
By Rakesh M. Verma, David J. Marchette
August 29, 2022
Cybersecurity Analytics is for the cybersecurity student and professional who wants to learn data science techniques critical for tackling cybersecurity challenges, and for the data science student and professional who wants to learn about cybersecurity adaptations. Trying to build a malware ...
Massive Graph Analytics
1st Edition
Edited
By David A. Bader
July 20, 2022
"Graphs. Such a simple idea. Map a problem onto a graph then solve it by searching over the graph or by exploring the structure of the graph. What could be easier? Turns out, however, that working with graphs is a vast and complex field. Keeping up is challenging. To help keep up, you just need an ...
Data Science: A First Introduction
1st Edition
By Tiffany Timbers, Trevor Campbell, Melissa Lee
July 15, 2022
Data Science: A First Introduction focuses on using the R 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. The text emphasizes workflows ...
Tree-Based Methods for Statistical Learning in R
1st Edition
By Brandon M. Greenwell
June 23, 2022
Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. Building a strong ...
Supervised Machine Learning for Text Analysis in R
1st Edition
By Emil Hvitfeldt, Julia Silge
October 22, 2021
Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, ...
Public Policy Analytics: Code and Context for Data Science in Government
1st Edition
By Ken Steif
August 20, 2021
Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory ...