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:
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.
By Michael C. Wimberly
May 08, 2023
The burgeoning field of data science has provided a wealth of techniques for analysing large and complex geospatial datasets, including descriptive, explanatory, and predictive analytics. However, applying these methods is just one part of the overall process of geographic data science. Other ...
By Sarah Lin, Dorris Scott
April 18, 2023
Librarians understand the need to store, use and analyze data related to their collection, patrons and institution, and there has been consistent interest over the last 10 years to improve data management, analysis, and visualization skills within the profession. However, librarians find it ...
By Peter Prevos
April 17, 2023
This addition to the Data Science Series introduces the principles of data science and R to the singular needs of water professionals. The book provides unique data and examples relevant to managing water utility and is sourced from the author’s extensive experience. The book is an applied, ...
By Hui Lin, Ming Li
April 14, 2023
This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities ...
By Nachshon (Sean) Goltz, Tracey Dowdeswell
April 13, 2023
In the midst of the 4th industrial revolution, big data is weighed in gold, placing enormous power in the hands of data scientists - the modern AI alchemists. But great power comes with greater responsibility. This book seeks to shape, in a practical, diverse and inclusive way, the ethical compass ...
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 ...
By Jerry Davis
March 06, 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; ...
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 ...
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 ...
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 ...
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 ...
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 ...
