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.
· 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.
Basketball Data Science: With Applications in R
Maya Gans, Toby Hodges, Greg Wilson
February 06, 2020
Paola Zuccolotto, Marica Manisera
January 23, 2020
Using data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an MBA player’s shots or doing an analysis of the impact of...
Rakesh M. Verma, David J. Marchette
November 20, 2019
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...
Rafael A. Irizarry
November 08, 2019
Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop...
Max Kuhn, Kjell Johnson
August 02, 2019
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset...
June 20, 2019
Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes...