Programme Parameters
Advanced Postgrad
3 Weeks (30 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
Data Stewardship for AI: Privacy & Quality is a multidisciplinary, compliance-aware course that prepares participants to manage the data behind AI—ethically, legally, and strategically. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Data Stewardship for AI: Privacy & Quality is a multidisciplinary, compliance-aware course that prepares participants to manage the data behind AI—ethically, legally, and strategically.
📋 Course Objectives
1. Translate AI Enablement theory into practical, reproducible analysis.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• Tangible, reproducible AI Enablement work to show supervisors or employers.
• 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
Principles and Roles in AI Data Stewardship
Learn the fundamentals of data stewardship in AI and its importance. • Understand the roles and responsibilities of data stewards in AI projects. • Explore data as a strategic asset and its ethics and governance.
Privacy-Centric Data Design
Understand data privacy in the context of AI and its legal frameworks. • Learn about personally identifiable information (PII) and sensitive data. • Discover consent, anonymization, and data minimization techniques.
Data Quality Dimensions and Standards
Define quality in AI datasets and understand its dimensions. • Identify common sources of bias and error in AI data. • Learn tools for validating and profiling AI data.
Data Lineage and Metadata Management
Understand why data lineage matters in AI and how to document it. • Learn metadata standards and how to create a data catalog for AI systems. • Discover how to maintain a data catalog for long-term stewardship.
Governance and Risk in AI Data Lifecycle
Build governance frameworks for AI data and conduct risk assessments. • Learn how to audit AI data pipelines for compliance and collaborate with cross-functional teams. • Understand the importance of governance and risk management in AI data lifecycle.
Future-Proof Stewardship and Capstone
Design scalable stewardship processes and monitor for drift, privacy breaches, and integrity loss. • Learn responsible data offboarding and retention strategies. • Complete a capstone project – design a stewardship plan for a real AI use case.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Data quality assessment |
| Covered Tool / Platform | data lineage |
| Covered Tool / Platform | metadata management |
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