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001437480 001__ 1437480
001437480 003__ OCoLC
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001437480 019__ $$a1257017967
001437480 020__ $$a9783030676261$$q(electronic bk.)
001437480 020__ $$a3030676269$$q(electronic bk.)
001437480 020__ $$z9783030676254
001437480 020__ $$z3030676250
001437480 0247_ $$a10.1007/978-3-030-67626-1$$2doi
001437480 035__ $$aSP(OCoLC)1257077801
001437480 040__ $$aEBLCP$$beng$$epn$$cEBLCP$$dGW5XE$$dYDX$$dOCLCO$$dOCLCF$$dN$T$$dUKAHL$$dOCLCQ$$dSFB$$dOCLCO$$dOCLCQ
001437480 049__ $$aISEA
001437480 050_4 $$aHF5549.5.D37
001437480 08204 $$a658.300285/631$$223
001437480 1001_ $$aRosett, Christopher M.
001437480 24510 $$aIntroducing HR analytics with machine learning :$$bempowering practitioners, psychologists, and organizations /$$cChristopher M. Rosett, Austin Hagerty.
001437480 260__ $$aCham, Switzerland :$$bSpringer,$$c2021.
001437480 300__ $$a1 online resource (266 pages)
001437480 336__ $$atext$$btxt$$2rdacontent
001437480 337__ $$acomputer$$bc$$2rdamedia
001437480 338__ $$aonline resource$$bcr$$2rdacarrier
001437480 500__ $$aIncludes index.
001437480 5050_ $$aPart I: Introducing Machine Learning: Past and Present -- The Historical Lens of Sub-Fields -- The State of the People Data Industry -- Part II: The Science, Philosophy, and Legality of using Machine Learning with People Data -- Scientific Considerations when Working with Behavioral Data -- Legal and Ethical Considerations when Working with Employee Data -- Part III: -- Instruction and Application of Machine Learning in an Employee Data Context -- Introduction and Overview of Stats and Computing -- Interpret and communicate -- Data Analyzing -- Data Wrangling.
001437480 506__ $$aAccess limited to authorized users.
001437480 520__ $$aThis book directly addresses the explosion of literature about leveraging analytics with employee data and how organizational psychologists and practitioners can harness new information to help guide positive change in the workplace. In order for todays organizational psychologists to successfully work with their partners they must go beyond behavioral science into the realms of computing and business acumen. Similarly, todays data scientists must appreciate the unique aspects of behavioral data and the special circumstances which surround HR data and HR systems. Finally, traditional HR professionals must become familiar with research methods, statistics, and data systems in order to collaborate with these new specialized partners and teams. Despite the increasing importance of this diversity of skill, many organizations are still unprepared to build teams with the comprehensive skills necessary to have high performing HR Analytics functions. And importantly, all these considerations are magnified by the introduction and acceleration of machine learning in HR. This book will serve as an introduction to these areas and provide guidance on building the connectivity across domains required to establish well-rounded skills for individuals and best practices for organizations when beginning to apply advanced analytics to workforce data. It will also introduce machine learning and where it fits within the larger HR Analytics framework by explaining many of its basic tenets and methodologies. By the end of the book, readers will understand the skills required to do advanced HR analytics well, as well as how to begin designing and applying machine learning within a larger human capital strategy.
001437480 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed July 6, 2021).
001437480 650_0 $$aPersonnel management$$xData processing.
001437480 650_0 $$aMachine learning.
001437480 650_6 $$aPersonnel$$xDirection$$xInformatique.
001437480 650_6 $$aApprentissage automatique.
001437480 655_7 $$aLlibres electrònics.$$2thub
001437480 655_0 $$aElectronic books.
001437480 7001_ $$aHagerty, Austin.
001437480 77608 $$iPrint version:$$aRosett, Christopher M.$$tIntroducing HR Analytics with Machine Learning.$$dCham : Springer International Publishing AG, ©2021$$z9783030676254
001437480 852__ $$bebk
001437480 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-67626-1$$zOnline Access$$91397441.1
001437480 909CO $$ooai:library.usi.edu:1437480$$pGLOBAL_SET
001437480 980__ $$aBIB
001437480 980__ $$aEBOOK
001437480 982__ $$aEbook
001437480 983__ $$aOnline
001437480 994__ $$a92$$bISE