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Introducing HR analytics with machin...

Hagerty, Austin.

Introducing HR analytics with machine learningempowering practitioners, psychologists, and organizations /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Introducing HR analytics with machine learningby Christopher M. Rosett, Austin Hagerty.
Reminder of title:	empowering practitioners, psychologists, and organizations /
Author:	Rosett, Christopher M.
other author:	Hagerty, Austin.
Published:	Cham :Springer International Publishing :2021.
Description:	vii, 271 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Personnel managementData processing.
Online resource:	https://doi.org/10.1007/978-3-030-67626-1
ISBN:	9783030676261$q(electronic bk.)

Introducing HR analytics with machine learningempowering practitioners, psychologists, and organizations /
Rosett, Christopher M.

Introducing HR analytics with machine learningempowering practitioners, psychologists, and organizations /[electronic resource] :by Christopher M. Rosett, Austin Hagerty. - Cham :Springer International Publishing :2021. - vii, 271 p. :ill., digital ;24 cm.

Part 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.

This 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.

ISBN: 9783030676261$q(electronic bk.)

Standard No.: 10.1007/978-3-030-67626-1doiSubjects--Topical Terms:

668747
Personnel management
--Data processing.

LC Class. No.: HF5549.5.D37

Dewey Class. No.: 658.300285631

Introducing HR analytics with machine learningempowering practitioners, psychologists, and organizations /
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245 1 0 $a Introducing HR analytics with machine learning $h [electronic resource] : $b empowering practitioners, psychologists, and organizations / $c by Christopher M. Rosett, Austin Hagerty.
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505 0 $a Part 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 $a This 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.
650 0 $a Personnel management $x Data processing. $3 668747
650 0 $a Machine learning. $3 188639
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