Data science foundations : geometry and topology of complex hierarchic systems and big data analytics / Fionn Murtagh.

Includes bibliographical references and index.

1. The correspondence analysis platform for mapping semantics --
2. Analysis and synthesis of narrative: semantics of interactivity --
3. Symmetry in data mining and analysis through hierarchy --
4. Geometry and topology of data analysis: in p-Adie terms --
5. Fast, linear time, m-adic hierarchical clustering --
6. Big data scaling through metric mapping --
7. On ultrametric algorithmic information --
8. Geometry and topology of Matte Blanco's bi-logic in psychoanalytics --
9. Ultrametric model of mind: application to text content analysis --
10. Concluding discussion on software environments.

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 UniversityData 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.