CHAPMAN & HALL/CRC DATA SCIENCE SERIES

Reflecting the interdisciplinary nature of the field, this 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.

The inclusion of concrete examples, applications, and methods is highly encouraged. The scope of the series includes titles in the areas of machine learning, pattern recognition, predictive analytics, business analytics, Big Data, visualization, programming, software, learning analytics, data wrangling, interactive graphics, and reproducible research.

Published Titles

Feature Engineering and Selection
A Practical Approach for Predictive Models
Max Kuhn and Kjell Johnson

Probability and Statistics for Data Science
Math + R + Data
Norman Matloff

Introduction to Data Science
Data Analysis and Prediction Algorithms with R
Rafael A. Irizarry

Cybersecurity Analytics
Rakesh M. Verma and David J. Marchette

Basketball Data Science
With Applications in R
Paola Zuccolotto and Marcia Manisera

JavaScript for Data Science
Maya Gans, Toby Hodges, and Greg Wilson

Statistical Foundations of Data Science
Jianqing Fan, Runze Li, Cun-Hui Zhang and Hui Zou

 

For more information about this series, please visit: https://www.crcpress.com/Chapman--HallCRC-Data-Science-Series/book-series/CHDSS