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020			_a9783030676261
024	7		_a10.1007/978-3-030-67626-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aHF5549.5.D37 _b2021 EB
100	1		_aRosett, Christopher M. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9681332
245	1	0	_a Introducing HR Analytics with Machine Learning : _bEmpowering Practitioners, Psychologists, and Organizations _cby Christopher M. Rosett, Austin Hagerty
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (VII, 271 páginas) _b62 ilustraciones, 8 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aBehavioral Science and Psychology (SpringerNature-41168)
490	0		_aBehavioral Science and Psychology (SpringerNature-43718)
505	0		_aPart I: Introducing Machine Learning: Past and Present -- The Historical Lens of Sub-Fields -- The State of the People Data Industry -- Part II: The Science, Philosophy, and Legality of using Machine Learning with People Data -- Scientific Considerations when Working with Behavioral Data -- Legal and Ethical Considerations when Working with Employee Data -- Part III: - Instruction and Application of Machine Learning in an Employee Data Context -- Introduction and Overview of Stats and Computing -- Interpret and communicate -- Data Analyzing -- Data Wrangling.
520	3		_aThis book directly addresses the explosion of literature about leveraging analytics with employee data and how organizational psychologists and practitioners can harness new information to help guide positive change in the workplace. In order for today's organizational psychologists to successfully work with their partners they must go beyond behavioral science into the realms of computing and business acumen. Similarly, today's data scientists must appreciate the unique aspects of behavioral data and the special circumstances which surround HR data and HR systems. Finally, traditional HR professionals must become familiar with research methods, statistics, and data systems in order to collaborate with these new specialized partners and teams. Despite the increasing importance of this diversity of skill, many organizations are still unprepared to build teams with the comprehensive skills necessary to have high performing HR Analytics functions. And importantly, all these considerations are magnified by the introduction and acceleration of machine learning in HR. This book will serve as an introduction to these areas and provide guidance on building the connectivity across domains required to establish well-rounded skills for individuals and best practices for organizations when beginning to apply advanced analytics to workforce data. It will also introduce machine learning and where it fits within the larger HR Analytics framework by explaining many of its basic tenets and methodologies. By the end of the book, readers will understand the skills required to do advanced HR analytics well, as well as how to begin designing and applying machine learning within a larger human capital strategy.
988			_aSpringer_Psychology_2021
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _aPsicología industrial _9139899
700	1		_aHagerty, Austin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9681333
776	0	8	_iPrinted edition _z9783030676254
776	0	8	_iPrinted edition _z9783030676278
776	0	8	_iPrinted edition _z9783030676285
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-67626-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2022 _dz _esc _zSI