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Mathematical Foundations Of Big Data Analytics 1st Ed 2021 Vladimir Shikhman

Name: Mathematical Foundations Of Big Data Analytics 1st Ed 2021 Vladimir Shikhman
SKU: BELL-51589028
Price: 31.00 USD
Availability: InStock
Rating: 4.1 (20 reviews)

SKU: BELL-51589028

$ 31.00 ~~$ 45.00~~ (-31%)

4.1

20 reviews

Mathematical Foundations Of Big Data Analytics 1st Ed 2021 Vladimir Shikhman instant download after payment.

Publisher: Springer Gabler

File Extension: PDF

File size: 1.42 MB

Pages: 288

Author: Vladimir Shikhman

ISBN: 9783662625200, 3662625202

Language: English

Year: 2021

Edition: 1st ed. 2021

Product desciption

Mathematical Foundations Of Big Data Analytics 1st Ed 2021 Vladimir Shikhman by Vladimir Shikhman 9783662625200, 3662625202 instant download after payment.

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.