- Home
- Social Science
- General
- Predictive Analytics in Human Resource Management (A Hands-on Approach)
Predictive Analytics in Human Resource Management (A Hands-on Approach)
- Availability: Confirm prior to ordering
- Branding: minimum 50 pieces (add’l costs below)
- Check Freight Rates (branded products only)
Branding Options (v), Availability & Lead Times
- 1-Color Imprint: $2.00 ea.
- Promo-Page Insert: $2.50 ea. (full-color printed, single-sided page)
- Belly-Band Wrap: $2.50 ea. (full-color printed)
- Set-Up Charge: $45 per decoration
- Availability: Product availability changes daily, so please confirm your quantity is available prior to placing an order.
- Branded Products: allow 10 business days from proof approval for production. Branding options may be limited or unavailable based on product design or cover artwork.
- Unbranded Products: allow 3-5 business days for shipping. All Unbranded items receive FREE ground shipping in the US. Inquire for international shipping.
- RETURNS/CANCELLATIONS: All orders, branded or unbranded, are NON-CANCELLABLE and NON-RETURNABLE once a purchase order has been received.
Product Details
Overview
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.








