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.
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 ...
Feature Engineering and Selection: A Practical Approach for Predictive Models
1st Edition
By Max Kuhn, Kjell Johnson
June 30, 2021
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 ...
Data Analytics: A Small Data Approach
1st Edition
By Shuai Huang, Houtao Deng
April 20, 2021
Data Analytics: A Small Data Approach is suitable for an introductory data analytics course to help students understand some main statistical learning models. It has many small datasets to guide students to work out pencil solutions of the models and then compare with results obtained from ...
An Introduction to IoT Analytics
1st Edition
By Harry G. Perros
March 04, 2021
This book covers techniques that can be used to analyze data from IoT sensors and addresses questions regarding the performance of an IoT system. It strikes a balance between practice and theory so one can learn how to apply these tools in practice with a good understanding of their inner workings....
A Tour of Data Science: Learn R and Python in Parallel
1st Edition
By Nailong Zhang
November 12, 2020
A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers...
Statistical Foundations of Data Science
1st Edition
By Jianqing Fan, Runze Li, Cun-Hui Zhang, Hui Zou
August 17, 2020
Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a ...
JavaScript for Data Science
1st Edition
By Maya Gans, Toby Hodges, Greg Wilson
January 28, 2020
JavaScript is the native language of the Internet. Originally created to make web pages more dynamic, it is now used for software projects of all kinds, including scientific visualization and data services. However, most data scientists have little or no experience with JavaScript, and most ...
Basketball Data Science: With Applications in R
1st Edition
By Paola Zuccolotto, Marica Manisera
January 14, 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 NBA player's shots or doing an analysis of the impact of high ...