8 Topics
5 Days
Programme Overview
Training Description
• HR Analytics Specialists
• Talent Acquisition Professionals
• HR Managers and Directors
• Workforce Planners
• People Data Analysts
• Organizational Development Professionals
• Learning & Development Consultants
• HR Technology Implementers
Session Objectives
- Understand the core concepts of AI and machine learning in HR analytics
- Apply predictive modeling to forecast attrition, performance, and engagement
- Build and interpret talent dashboards and AI-generated insights
- Integrate workforce data from multiple sources for comprehensive analysis
- Leverage machine learning tools for bias detection and inclusive hiring
About the Course
AI-Powered Talent Analytics and Predictive Modeling is a transformative training course designed to empower HR professionals and data analysts with the skills to harness artificial intelligence and predictive modeling to drive strategic workforce decisions. As organizations increasingly turn to data-driven HR, the ability to analyze employee data, forecast talent needs, and predict workforce trends using machine learning has become a critical advantage. This course introduces modern HR analytics tools and methods for predictive hiring, attrition modeling, performance optimization, and skills gap analysis—equipping participants to unlock insights from people data and align talent strategies with organizational goals.
Curriculum & Topics
8 Topics | 5 Days
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Subtopic 1.1:
Understanding AI and machine learning concepts in a people context
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Subtopic 1.2:
Benefits and challenges of implementing AI in HR
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Subtopic 1.3:
Data types and sources in HR analytics
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Subtopic 1.4:
Introduction to supervised and unsupervised learning
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Subtopic 1.5:
Key AI tools and platforms used in talent analytics
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Subtopic 2.1:
Cleaning and transforming HR datasets
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Subtopic 2.2:
Encoding categorical data (e.g. departments, education levels)
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Subtopic 2.3:
Deriving features such as tenure, promotion history, skill levels
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Subtopic 2.4:
Handling missing and sensitive data
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Subtopic 2.5:
Creating time-based features for attrition and promotion modeling
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Subtopic 3.1:
Building classification models for attrition prediction
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Subtopic 3.2:
Identifying drivers of turnover using SHAP and feature importance
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Subtopic 3.3:
Segmenting employees into risk profiles
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Subtopic 3.4:
Designing retention strategies based on model outputs
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Subtopic 3.5:
Evaluating model accuracy with confusion matrix and ROC curves
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Subtopic 4.1:
Using regression models for performance prediction
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Subtopic 4.2:
Correlating KPIs with behavioral and engagement data
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Subtopic 4.3:
Designing composite talent scores
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Subtopic 4.4:
Predicting high-potential employees
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Subtopic 4.5:
Benchmarking against historical performance patterns
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Subtopic 5.1:
Using analytics to forecast hiring and promotion needs
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Subtopic 5.2:
Mapping current vs future skill requirements
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Subtopic 5.3:
Predicting reskilling and training needs across teams
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Subtopic 5.4:
Using clustering to identify workforce segments
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Subtopic 5.5:
Scenario planning and capacity forecasting
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Subtopic 6.1:
Optimizing sourcing strategies with applicant funnel data
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Subtopic 6.2:
Using AI to assess candidate-job fit
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Subtopic 6.3:
Identifying unconscious bias in hiring pipelines
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Subtopic 6.4:
Predicting candidate success and cultural alignment
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Subtopic 6.5:
Enhancing diversity with AI-based fairness metrics
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Subtopic 7.1:
Creating interactive HR dashboards with Power BI or Tableau
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Subtopic 7.2:
Visualizing key metrics: turnover, engagement, diversity, performance
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Subtopic 7.3:
Sharing insights across HR and executive teams
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Subtopic 7.4:
Designing predictive dashboards with filters and drilldowns
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Subtopic 7.5:
Communicating AI insights clearly to stakeholders
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Subtopic 8.1:
Understanding legal and ethical implications of AI in HR
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Subtopic 8.2:
Ensuring data privacy and employee consent
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Subtopic 8.3:
Building explainable and auditable ML models
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Subtopic 8.4:
Developing AI governance frameworks in HR
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Subtopic 8.5:
Case studies on responsible use of AI in workforce decisions
