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 in Performance Management and Employee Engagement explores how people data, used carefully, can make performance and engagement fairer and more insightful. You learn to apply AI to performance analytics, engagement and sentiment analysis, and predicting attrition and drivers of engagement. Because these models touch people’s careers and morale, fairness, bias and privacy are central threads, not afterthoughts. The course connects analytics to better, more equitable HR decisions. You finish able to apply AI to performance and engagement responsibly. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI in performance management and employee engagement — analysing performance and sentiment data to support fair reviews, engagement and retention.
📋 Course Objectives
1. Analyse performance data for insight.
2. Measure engagement and sentiment.
3. Predict attrition and engagement drivers.
4. Support fair, unbiased reviews.
5. Protect privacy and mitigate bias.
👥 Who Should Enroll?
• HR and people-analytics professionals
• Managers and team leaders
• HR-technology teams
• Students of people analytics
🚀 Key Learning Outcomes
• An understanding of AI in performance management.
• An engagement-analytics perspective.
• A fairness-first HR approach.
• 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
What Performance Data Can Honestly Support
• Output, outcome and behaviour measures, and which are gameable
• Rater bias, recency effects and central tendency in review data
• Engagement surveys: construct validity, response bias and survey fatigue
Modelling Performance and Engagement
• Text analytics on review comments and open-ended feedback
• Driver analysis that separates correlation from actionable lever
• Team and manager effects, and the small-sample problem in unit-level analysis
Continuous Feedback and Development
• Goal setting and check-in cadences supported by analytics
• Personalised development recommendations without prescriptive career tracks
• Language-model assistance in writing reviews, and the homogenisation risk
Surveillance, Trust and Law
• The line between performance analytics and employee surveillance
• Transparency and consultation obligations in different jurisdictions
• Adverse impact testing where analytics informs pay or promotion
Implementation That Managers Accept
• Manager enablement and interpretation training
• Avoiding forced distribution and metric fixation
• Evaluating whether the system improved performance or only measurement
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Microsoft Excel |
| Covered Tool / Platform | Relevant Online Databases |
Frequently Asked Questions
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