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Books > Computing & IT > Applications of computing > Databases
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Mathematical Foundations of Big Data Analytics (Paperback, 1st ed. 2021)
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Mathematical Foundations of Big Data Analytics (Paperback, 1st ed. 2021)
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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.
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