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HRA-EMBA-18
MBA(Exe.) 2018-19: Term-IV

HR Analytics
Credits1.5
Faculty NameDr Arup Roy Chowdhury
ProgramEMBA
Academic Year and Term2018-19 (Term IV)

1. Course Description

In today’s competitive business scenario, the field of analytics has gained importance to describe, predict, interpret and communicate meaningful patterns from data. This inference further helps in taking appropriate business decisions. In majority of business scenarios, decision making plays an important role. It is imperative to make decisions, within the limited information that is made available. It is always better to base the decisions on sound logic or an explanation, which is where some of the tools learned through this course, may come in handy to the participants. The aim of the course would be to relate the business statistics to the practical business-related settings specifically related to the domain of Human Resources. Taking cue from the employee life cycle process the course aims to demonstrate the application of analytics to some of the key decisions related to areas like hiring, retention, performance, staffing, effectiveness of training, compensation etc.

2. Student Learning Outcomes
· Be able to demonstrate the application of business statistics to business situations.
· Be able to use appropriate analytical tool for business problems.
· Be able to take appropriate decision and support their decisions using appropriate inferences from the analysis.
3. Suggested Text Books/ Reading Material
· Albright Christian, S., & Winston L. Wayne. Business Analytics: Data Analysis and Decision Making
· Bassi, L., Carpenter, R., & McMurrer, D. (2010). HR Analytics Handbook: Report of the State of Knowledge.
· Case studies
4.

Tentative Session Plan

Session Number
Topics/Activities
Topics
1
Recap of basic statistics. Measures of central tendency, central limit theorem, basics of distributions, T-test, ANOVA, Chi-Square
2
Recap of Regression and various models of regressionVariable (Dependent vs. Independent Variable), assumptions of regression, various models of regression.
3
Correlation vis-à-vis causality, Introduction to Analytics and specifically the field of HR analytics and its applicationMethods of determining causality and testing of hypothesis. Null versus alternate, analytics and HR specific applications and its possibilities. Descriptive, predictive and prescriptive analytics
4-6
Application of HR Analytics to employee selection, determining effectiveness of training, performance management and wage modelling
7-8
Making sense of qualitative dataBasics of arriving at inferences from qualitative data
9-10
Project Presentation

Pre-requisites of the course

The participants are expected to be thorough with the basic statistics and its applications. The participants are also expected to be familiar with basics of MS Excel. Some knowledge of working on packages such as R/SAS/STATA/SPSS would be an added advantage.

Pedagogy

A mix of lecture and case study-based approach would be adopted. The instructor would also facilitate the hands-on training for the exercises using relevant tools.

5. Evaluation

Sl. No.
Component
Weightage (%)
1
Quiz
20
2
End Term Exam
50
3
Individual Project
30

The institute norms for attendance and grading would be followed. No requests for grace marks, increase in the grades/marks would be entertained after declaration of the results.

6. Academic Integrity

Students are expected to show the highest level of academic integrity in their submissions and assignments. Please note that students involved in academic dishonesty will be dealt with as per the Manual of Policies.

Academic dishonesty consists of misrepresentation by deception or by other fraudulent means. In an academic setting this may take any number of forms such as copying or use of unauthorized aids in tests, assignments, examinations, term papers, or cases; plagiarism; talking during in-class examinations; submission of work that is not your own without citation; submission of work generated for another course without prior clearance by the instructor of both courses; submission of work generated by another person; aiding and abetting another student’s dishonesty; and giving false information for the purpose of gaining credits.

Created By: Alora Kar on 11/17/2018 at 09:34 AM
Category: MBA(Exe.)2018-19 T-IV Doctype: Document

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