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008			230831s2023 gw | o |||| 0|eng d
020			_a9783662678824
024	7		_a10.1007/978-3-662-67882-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA276 _b2023 EB
100	1		_aPlaue, Matthias _d1976- _eautor _4http://id.loc.gov/vocabulary/relators/aut _9689428
245	0	0	_aData Science : _bAn Introduction to Statistics and Machine Learning _cby Matthias Plaue
250			_a1st ed 2023
264		1	_aBerlin Heidelberg _bSpringer Berlin Heidelberg _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aPreface -- Part I Basics -- 1 Elements of data organization -- 2 Descriptive statistics -- Part II Stochastics -- 3 Probability theory -- 4 Inferential statistics -- 5 Multivariate statistics -- Part III Machine learning -- 6 Supervised machine learning -- 7 Unsupervised machine learning -- 8 Applications of machine learning -- Appendix -- A Exercises with answers -- B Mathematical preliminaries -- Supplementary literature -- Index.
520			_aData science is the discipline of transforming data into valuable insights. It helps you understand and predict complex and uncertain phenomena, from pandemics to economics. It also drives many influential technologies today, such as web search, image recognition, and AI assistants. This textbook covers the mathematical foundations and core topics of data science in a comprehensive and rigorous way, including data modeling, statistics, probability, and machine learning. You will learn essential tools, like clustering, dimensionality reduction, and neural networks, as well as how to use them to solve real-world problems with actual datasets and exercises. This book is suitable for professionals, students, and instructors who want to master the theory of data science and explore its applications across various domains. The book requires some prior knowledge of calculus and linear algebra but provides a quick review of these topics in the appendix. About the author Matthias Plaue is a versatile researcher with a background in mathematical physics. He has explored diverse domains, spanning from relativity theory to pedestrian dynamics. As a data scientist, he develops algorithms for data analysis and artificial intelligence, tailored to support strategic decision-making. In addition to his professional pursuits, he has devoted considerable time to mentoring students, imparting a deep understanding of mathematics and its practical application in tackling complex problems across the fields of science, technology, and engineering.
988			_aSpringer_Computer_2023
650		7	_2embne _9138936 _aEstadística matemática
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-662-67882-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2024 _dz _eb _zSI