Programme Parameters
Advanced Postgrad
8 Weeks (80 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI for HR Analytics and Decision Making shows how people data, used well, sharpens workforce decisions — and how, used carelessly, it does harm. You build models for the core HR problems: predicting attrition, screening and matching talent, and forecasting workforce needs, working with realistic HR data. Running through the course is an unavoidable theme: fairness, bias and privacy, because HR models decide people’s livelihoods and face growing legal scrutiny. You finish able to build HR analytics that are useful, defensible and fair. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to human-resources analytics — talent acquisition, attrition prediction, performance and workforce planning — with a strong focus on fairness.
📋 Course Objectives
1. Analyse HR data for workforce insight.
2. Build attrition-prediction and retention models.
3. Apply AI to talent screening and matching.
4. Forecast workforce and capacity needs.
5. Audit HR models for fairness, bias and privacy.
👥 Who Should Enroll?
• HR and people-analytics professionals
• Data scientists working with workforce data
• HR technology and operations teams
• Students specialising in people analytics
🚀 Key Learning Outcomes
• The ability to build defensible HR analytics.
• An attrition or workforce-planning project.
• A fairness-first approach to people data.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
People Data and Its Constraints
• 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
Workforce Analytics That Withstand Scrutiny
• 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
Attrition and Performance Modelling
• 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
Selection Tools and Legal Exposure
• 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
Decision Support and Governance
• Presenting uncertainty to managers who want a single number
• Human review requirements for consequential employment decisions
• Monitoring deployed tools for drift and disparate outcomes
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
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