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DATA SCIENCE FOUNDATIONS: GEOMETRY AND TOPOLOGY OF COMPLEX HIERARCHIC SYSTEMS AND BIG DATA ANALYTICS
Título:
DATA SCIENCE FOUNDATIONS: GEOMETRY AND TOPOLOGY OF COMPLEX HIERARCHIC SYSTEMS AND BIG DATA ANALYTICS
Subtítulo:
Autor:
MURTAGH, F
Editorial:
CRC PRESS
Año de edición:
2017
ISBN:
978-1-4987-6393-6
Páginas:
230
88,95 €

 

Sinopsis

Features

Takes an approach based on the geometry and topology of complex hierarchic systems.
Provides a good balance of rigour, mathematics, and computational thinking.
Features case studies from various fields, including social media, psychoanalysis, and cosmology.
Data sets and software code, mostly in R, can be downloaded from the book website: http://www.DataScienceGeometryTopology.info.


Summary

´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.



Table of Contents

Preface
Part I. Narratives from Film and Literature, from Social Media and Contemporary Life
The Correspondence Analysis Platform for Mapping Semantics
Analysis and Synthesis of Narrative: Semantics of Interactivity
Part II. Foundations of Analytics through the Geometry and Topology of Complex Systems
Symmetry in Data Mining and Analysis through Hierarchy
Geometry and Topology of Data Analysis: in p-Adic Terms
Part III. New Challenges and New Solutions for Information Search and Discovery
Fast, Linear Time, m-Adic Hierarchical Clustering
Big Data Scaling through Metric Mapping
Part IV. New Frontiers: New Vistas on Information, Cognition and on the Human Mind
On Ultrametric Algorithmic Information
Geometry and Topology of Matte Blanco´s Bi-Logic in Psychoanalytics
Ultrametric Model of Mind: Application to Text Content Analysis
Concluding Discussion on Software Environments