Mathematical Foundations of Big Data Analytics

Authors: Shikhman, Vladimir, Müller, David

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  • Covers all relevant techniques commonly used in Big Data Analytics​
  • Standardized structure and size of the chapters: motivation, results, case-study, exercises
  • Recommended and developed for university courses in Germany, Austria and Switzerland
  • Provides complete solutions for the exercises
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eBook 24,60 €
price for France (gross)
  • ISBN 978-3-662-62521-7
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 31,64 €
price for France (gross)
  • ISBN 978-3-662-62520-0
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions & severe weather in the US may cause delays
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
About this Textbook

In this textbook, basic mathematical models used in Big Data Analytics are presented and application-oriented references to relevant practical issues are made. Necessary mathematical tools are examined and applied to current problems of data analysis, such as brand loyalty, portfolio selection, credit investigation, quality control, product clustering, asset pricing etc. – mainly in an economic context. In addition, we discuss interdisciplinary applications to biology, linguistics, sociology, electrical engineering, computer science and artificial intelligence. For the models, we make use of a wide range of mathematics – from basic disciplines of numerical linear algebra, statistics and optimization to more specialized game, graph and even complexity theories. By doing so, we cover all relevant techniques commonly used in Big Data Analytics.Each chapter starts with a concrete practical problem whose primary aim is to motivate the study of a particular Big Data Analytics technique. Next, mathematical results follow – including important definitions, auxiliary statements and conclusions arising. Case-studies help to deepen the acquired knowledge by applying it in an interdisciplinary context. Exercises serve to improve understanding of the underlying theory. Complete solutions for exercises can be consulted by the interested reader at the end of the textbook; for some which have to be solved numerically, we provide descriptions of algorithms in Python code as supplementary material.This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland.

About the authors

Vladimir Shikhman is a professor of Economathematics at Chemnitz University of Technology.David Müller is one of his doctoral students.

Table of contents (10 chapters)

Table of contents (10 chapters)
  • Ranking

    Pages 1-20

    Shikhman, Vladimir (et al.)

  • Online Learning

    Pages 21-39

    Shikhman, Vladimir (et al.)

  • Recommendation Systems

    Pages 41-61

    Shikhman, Vladimir (et al.)

  • Classification

    Pages 63-85

    Shikhman, Vladimir (et al.)

  • Clustering

    Pages 87-105

    Shikhman, Vladimir (et al.)

Buy this book

eBook 24,60 €
price for France (gross)
  • ISBN 978-3-662-62521-7
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 31,64 €
price for France (gross)
  • ISBN 978-3-662-62520-0
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions & severe weather in the US may cause delays
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
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Bibliographic Information

Bibliographic Information
Book Title
Mathematical Foundations of Big Data Analytics
Authors
Copyright
2021
Publisher
Gabler Verlag
Copyright Holder
Springer-Verlag GmbH Germany, part of Springer Nature
eBook ISBN
978-3-662-62521-7
DOI
10.1007/978-3-662-62521-7
Softcover ISBN
978-3-662-62520-0
Edition Number
1
Number of Pages
XI, 273
Number of Illustrations
32 b/w illustrations, 21 illustrations in colour
Topics