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1. Course Description

We live in an era of information overload. Information essentially is data. This is true both in personal as well as work settings. The field of analytics comes in handy, to make sense of the data to describe, predict, or 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 (typically 3-5 bullet points)

· Be able to demonstrate the application of business statistics to business situations.
· Be able to demonstrate the use of appropriate analytical tool for business problems.
· Be able to take appropriate decisions and support their decisions using appropriate inferences from the analysis

3. Required Text Books and Reading Material

· Reference book: Bassi, L., Carpenter, R., & McMurrer, D. (2010). HR Analytics Handbook: Report of the State of Knowledge.
· Reference Book: Gujarati, D. N. (2003). Basic Econometrics. 4th. New York: McGraw-Hill.
· Textbook/Relevant Case studies – To be decided

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, Annova, Chi-Square
2	Recap of Regression and various models of regression	Variable (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 application	Methods of determining causality and testing of hypothesis. Null versus alternate, analytics and HR specific applications and its possibilities. Descriptive, predictive and prescriptive analytics
4	Understand the Decision making at different levels and the importance of proper measurement/collection of the data	Understand the decision making at three main levels. The need for analytics and diagnostics at middle management and higher management levels
5	Application of HR Analytics to Hiring/Retention/Attrition
6	Application of HR Analytics to Performance Management	Biases in performance measuring, separating signal and noise
7	Application of HR Analytics to Compensation and Reward Management	Incentive/reward design/ wage modelling
8 - 9	Making sense of qualitative data	Basics of arriving at inferences from qualitative data
10	Some other applications and future directions

Sr. No.	Component	Weightage (%)	Type
1	Mid Term (online, objectives)	30	Individual
2	End Term Exam	30	Individual
3	Group Project	40	Group
4	Total	100

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.

NO NETWORK POLICY

All students are requested not to operate any network enabled devices such as cell phones, tabs, ipads or any other electronic network enabled devices inside the classroom during the sessions. In case you are compelled to carry it in person, you may keep it in the switched off mode. Anyone found to operate such devices during the session timings shall get penalized with a 10 marks deduction from the total evaluation scores for every incident of violation noted by the facilitator. The instructor may also impose any other suitable penalty as deterrence. No discussion or negotiation will be entertained at all with respect to this.