000			03583nam a22003497i 4500
003			UIASF
005			20240105153913.0
008			210707t20192019sz ||||gr|||| 00| 0 eng d
020			_a9783030118204
035			_a445066
050		4	_aHF 5548.2 _bA67.2019
040			_aUIASF _bspa _erda _cUIASF _dUIASF
245	0	0	_aApplied data science : _blessons learned for the data driven business / _cMartin Braschler, Thilo Stadelmann, Kurt Stockinger editors.
264		1	_aWinterthur, Switzerland : _bSpringer Nature, _c2019,
264		4	_c©2019
300			_axiii, 465 páginas ; _c24 cm
336			_atexto _btxt _2rdacontent
337			_asin mediación _bn _2rdamedia
338			_avolumen _bnc _2rdacarrier
520	3		_aThis bavolumen _bnc _2rdacarrierook has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors - some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors' combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.
650		4	_aBig data
650		4	_aNegocios _xProcesamiento de datos
650		4	_aAdministración _xProcesamiento de datos
700	1		_aBraschler, Martin _eeditor
700	1		_aStadelman Thilo _eeditor
700	1		_aStockinger, Kurt _eeditor
942			_2lcc _cNEWBFXC1
999			_c697064 _d697064
980			_851 _gRonald RUIZ