Programme Content
Day 1: Introduction to Workforce Analytics using Data
Basics of Statistical Methods: Key concepts and relevance to HR.
Overview of workforce data: Sources, collection, and cleaning.
Descriptive statistics and exploratory data analysis.
Application: Analyzing employee satisfaction surveys to identify key drivers of satisfaction.
Day 2: Regression Analysis for HR Decision-Making
Simple and multiple linear regression models.
Applications in HR: Predicting employee turnover and performance.
Assumptions and diagnostics of regression models.
Application: Using regression analysis to assess the impact of remote work policies on employee productivity.
Day 3: Advanced Modelling Techniques
Logistic regression for binary outcomes (e.g., promotion likelihood).
Panel data analysis: Understanding employee behavior over time.
Time series models for forecasting workforce needs.
Application: Forecasting workforce demand in seasonal industries using time series models.
Day 4: Applications in HR Policy Evaluation
Difference-in-differences (DiD) analysis to measure policy impacts.
Propensity score matching for evaluating training programs.
Employee satisfaction surveys and structural equation modeling (SEM).
Application: Evaluating the effectiveness of DEI (Diversity, Equity, Inclusion) initiatives using DiD analysis.
