Mathematical Foundations of Big Data Analytics

Vladimir Shikhman

ISBN 10: 3662625202 / ISBN 13: 9783662625200

Editore: Springer Gabler, 2021

Brand New ! Fast Delivery "International Edition " and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 4-6 Working days .and we do have flat rate for up to 2LB. Extra shipping charges will be requested This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability. Codice articolo At 9783662625200

Riassunto:

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.

Informazioni sull'autore:

Vladimir Shikhman is a professor of Economathematics at Chemnitz University of Technology.

David Müller is one of his doctoral students.

Dati bibliografici

Titolo: Mathematical Foundations of Big Data ...
Casa editrice: Springer Gabler
Data di pubblicazione: 2021
Legatura: Paperback
Condizione: New
Edizione: Edizione Internazionale