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.

Duration: 5 Days

Target Audience

• HR Analytics Specialists
• Talent Acquisition Professionals
• HR Managers and Directors
• Workforce Planners
• People Data Analysts
• Organizational Development Professionals
• Learning & Development Consultants
• HR Technology Implementers

Course 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
• Improve decision-making in recruitment, promotion, and retention
• Translate business questions into analytical models using HR data
• Design ethical and explainable AI models for people analytics

Module 1: Foundations of AI in HR Analytics

• Understanding AI and machine learning concepts in a people context
• Benefits and challenges of implementing AI in HR
• Data types and sources in HR analytics
• Introduction to supervised and unsupervised learning
• Key AI tools and platforms used in talent analytics

Module 2: Data Preparation and Feature Engineering for HR Data

• Cleaning and transforming HR datasets
• Encoding categorical data (e.g. departments, education levels)
• Deriving features such as tenure, promotion history, skill levels
• Handling missing and sensitive data
• Creating time-based features for attrition and promotion modeling

Module 3: Predictive Modeling for Employee Attrition and Retention

• Building classification models for attrition prediction
• Identifying drivers of turnover using SHAP and feature importance
• Segmenting employees into risk profiles
• Designing retention strategies based on model outputs
• Evaluating model accuracy with confusion matrix and ROC curves

Module 4: Performance Forecasting and Talent Scoring

• Using regression models for performance prediction
• Correlating KPIs with behavioral and engagement data
• Designing composite talent scores
• Predicting high-potential employees
• Benchmarking against historical performance patterns

Module 5: Workforce Planning and Skill Gap Prediction

• Using analytics to forecast hiring and promotion needs
• Mapping current vs future skill requirements
• Predicting reskilling and training needs across teams
• Using clustering to identify workforce segments
• Scenario planning and capacity forecasting

Module 6: Talent Acquisition Analytics and Bias Detection

• Optimizing sourcing strategies with applicant funnel data
• Using AI to assess candidate-job fit
• Identifying unconscious bias in hiring pipelines
• Predicting candidate success and cultural alignment
• Enhancing diversity with AI-based fairness metrics

Module 7: Data Visualization and Dashboarding for People Insights

• Creating interactive HR dashboards with Power BI or Tableau
• Visualizing key metrics: turnover, engagement, diversity, performance
• Sharing insights across HR and executive teams
• Designing predictive dashboards with filters and drilldowns
• Communicating AI insights clearly to stakeholders

Module 8: Ethics, Privacy, and Governance in AI Talent Analytics

• Understanding legal and ethical implications of AI in HR
• Ensuring data privacy and employee consent
• Building explainable and auditable ML models
• Developing AI governance frameworks in HR
• Case studies on responsible use of AI in workforce decisions

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.