Predictive Analytics in Human Resource Management

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A01=Sahil Raj
A01=Shivinder Nijjer
Artificial neural networks
Author_Sahil Raj
Author_Shivinder Nijjer
Big Data Analytics
Business Problem
Category=KJ
Category=KJC
Category=KJMV2
Category=KJMV6
Category=PBT
employee attrition modelling
Employee Engagement
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eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Explaining Turnover Intention
Hidden Layers
Holistic Approach
Hr Analytic
Hr Data
Hr Function
Hr Leader
Hr Practice
Hr Scorecard
HRM
Human resource management
IoT Analytic
IoT Data Analytic
Job Function
K-nearest neighbour
Key Performance Indicators
Klynveld Peat Marwick Goerdeler
KNN Model
Lift Chart
Min Max Normalisation
Misclassification Error
Myers-Briggs
organisational data analysis
Predictive Analytics
predictive modelling for HR decision making
Press Trust of India
Python
R programming applications
recruitment data science
Roc Curve
Target Variables
Turnover Intent
turnover prediction models
Wearable Sensors
workforce analytics

Product details

  • ISBN 9780367460860
  • Weight: 360g
  • Dimensions: 156 x 234mm
  • Publication Date: 04 Dec 2020
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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This volume is a step-by-step guide to implementing predictive data analytics in human resource management (HRM). It demonstrates how to apply and predict various HR outcomes which have an organisational impact, to aid in strategising and better decision-making.

The book:

  • Presents key concepts and expands on the need and role of HR analytics in business management.
  • Utilises popular analytical tools like artificial neural networks (ANNs) and K-nearest neighbour (KNN) to provide practical demonstrations through R scripts for predicting turnover and applicant screening.
  • Discusses real-world corporate examples and employee data collected first-hand by the authors.
  • Includes individual chapter exercises and case studies for students and teachers.

Comprehensive and accessible, this guide will be useful for students, teachers, and researchers of data analytics, Big Data, human resource management, statistics, and economics. It will also be of interest to readers interested in learning more about statistics or programming.

Shivinder Nijjer is a faculty member at Chitkara Business School, Chitkara University, Punjab, India. She has also previously worked as a software engineer with Infosys Technologies Limited. She has a PhD in predictive analytics and has contributed extensively to publications in the field of management information systems and business analytics. She has published various research articles in eminent ABDC-ranked and Scopus Indexed Journals. She is also a reviewer for Scopus indexed journals. She has also been actively involved in designing and delivery of analytics courses for students.

Sahil Raj is a faculty member at School of Management Studies, Punjabi University, Patiala, India. He has a PhD in information systems and has previously worked with Ranbaxy Laboratories. His recent publications include Management Information Systems (2017) and Business Analytics (2015). He has also published numerous research papers, and is a reviewer and on the editorial board of many national and international journals. He has been invited as an expert speaker and trainer in analytics by various national institutions.

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