000			a
999			_c32044 _d32044
008			230618b xxu||||| |||| 00| 0 eng d
020			_a9783662625200
082			_a005.7 _bSHI
100			_aShikhman, Vladimir
245			_aMathematical foundations of big data analytics
260			_bSpringer Gabler, _c2021 _aGermany :
300			_axi, 273 p. ; _bill., _c24 cm
365			_d32.99 _bEUR _c93.50
504			_aIncludes bibliographical references and index.
520			_aIn 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.
650			_aBig data Mathematics
700			_aMuller, David
942			_2ddc _cBK