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Mathematical Foundations of Big Data Analytics

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Mathematical Foundations of Big Data Analytics/ by Vladimir Shikhman, David Müller.
Author:	Shikhman, Vladimir.
other author:	Müller, David.
Description:	XI, 273 p. 53 illus., 21 illus. in color. Textbook for German language market.online resource. :
Contained By:	Springer Nature eBook
Subject:	Big data. -
Online resource:	https://doi.org/10.1007/978-3-662-62521-7
ISBN:	9783662625217

Mathematical Foundations of Big Data Analytics
Shikhman, Vladimir.

Mathematical Foundations of Big Data Analytics[electronic resource] /by Vladimir Shikhman, David Müller. - 1st ed. 2021. - XI, 273 p. 53 illus., 21 illus. in color. Textbook for German language market.online resource.

Preface -- 1 Ranking -- 2 Online Learning -- 3 Recommendation Systems -- 4 Classification -- 5 Clustering -- 6 Linear Regression -- 7 Sparse Recovery -- 8 Neural Networks -- 9 Decision Trees -- 10 Solutions.

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

ISBN: 9783662625217

Standard No.: 10.1007/978-3-662-62521-7doiSubjects--Topical Terms:

981821
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

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