| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Moderate |
| Duration | 3 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹10749 / $124 |
| Tools | Data quality assessment data lineage metadata management |
About the Data Stewardship for AI Privacy and Quality 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.
This program focuses on creating data pipelines that are high-quality, privacy-preserving, regulation-compliant, and audit-ready—empowering professionals to build AI systems that are fair, explainable, and safe.
Program Highlights
• Comprehensive coverage of Data Stewardship for AI Privacy and Quality from fundamentals to advanced applications
• Hands-on projects and real-world case studies in AI
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: Data quality assessment, data lineage, metadata management
• Career-oriented training for academic and professional growth in AI
Course Curriculum
Module 1: 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.
Module 2: 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.
Module 3: 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.
Module 4: 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.
Module 5: 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.
Module 6: 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.
Tools, Techniques, or Platforms Covered
Data quality assessment data lineage metadata management
Real-World Applications
- Apply Data Stewardship for AI Privacy and Quality skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical AI competencies
- Solve industry-relevant problems using Data Stewardship for AI Privacy and Quality methodologies and tools
- Contribute to open-source projects and collaborative research in AI
- Prepare for competitive examinations, interviews, and professional certifications in AI
Who Should Attend & Prerequisites
- Industry-recognized e-Certification + e-Marksheet from NSTC
- Hands-on training with practical projects and industrial datasets
- Dedicated expert mentorship and doubt resolution
Certification
