📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI for HR Analytics and Decision Making, from foundations to a certified capstone project.
Foundations
• HR data sources, quality problems and small-sample realities
• Employee privacy expectations and works-council or union considerations
• Distinguishing questions worth answering from surveillance dressed as analytics
Descriptive
• Turnover, mobility and time-to-hire measured without misleading denominators
• Compensation analysis and pay-equity testing with appropriate controls
• Segmentation that does not become proxy discrimination
Predictive
• Attrition modelling, its weak signal and the ethics of acting on it
• Performance prediction and the contaminated-label problem
• Causal thinking: intervention effects rather than correlational risk scores
Hiring
• Resume screening and structured assessment, and documented adverse impact
• Four-fifths rule testing and bias audit requirements such as NYC Local Law 144
• Candidate notice, explanation and appeal rights
Practice
• Presenting uncertainty to managers who want a single number
• Human review requirements for consequential employment decisions
• Monitoring deployed tools for drift and disparate outcomes
Earn government-registered certification in AI for HR Analytics and Decision Making
e-Certificate and e-Marksheet issued on successful completion.
