Fionn Murtagh Livres

Focusing on correspondence analysis, this book offers a comprehensive introduction to its theory, methods, and practical applications. It emphasizes data coding and presents Java and R software for analysis, clustering, and interpretation. A dedicated chapter features case studies that illustrate the technique's use in various fields, including financial modeling, shape analysis, and biosciences, particularly with time-evolving data. Additionally, readers can access all software and datasets through a supporting website, enhancing the practical utility of the content.

"Data Science Foundations is most welcome and, indeed, a piece of literature that the field is very much in need of...quite different from most data analytics texts which largely ignore foundational concepts and simply present a cookbook of methods...a very useful text and I would certainly use it in my teaching." - Mark Girolami, Warwick University Data Science encompasses the traditional disciplines of mathematics, statistics, data analysis, machine learning, and pattern recognition. This book is designed to provide a new framework for Data Science, based on a solid foundation in mathematics and computational science. It is written in an accessible style, for readers who are engaged with the subject but not necessarily experts in all aspects. It includes a wide range of case studies from diverse fields, and seeks to inspire and motivate the reader with respect to data, associated information, and derived knowledge.