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Data science foundations : geometry and topology of complex hierarchic systems and big data analytics

By: Murtagh, Fionn.
Material type: materialTypeLabelBookSeries: Chapman & Hall CRC computer science and data analysis series.Publisher: Boca Raton : CRC Press, 2018Description: xviii, 205 p. ; ill., 26 cm.ISBN: 9781498763936.Subject(s): Big data | Ultrametic model | Wreath product group | Semantics | Human mind | Information cognition | Database administration | Spatial analysis | Quantitative research | MathematicsDDC classification: 005.701 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 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.
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Books 005.701 MUR (Browse shelf) Available 034635

Includes bibliographical references and index.

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.

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