Data Stewardship for AI: Privacy & Quality
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Course Overview
Table of Contents
Program Syllabus
Module 1
About
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. As AI systems increasingly influence decisions and operations across industries, the quality and governance of their training data become critical. 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.
Module 2
Aim
To train professionals in the principles and practices of responsible data stewardship for AI systems, with a focus on ensuring data privacy, quality, integrity, and governance throughout the AI development lifecycle.
Lead Instructor
Dr. Sarah Chen
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
Limited SeatsClosing Soon
Data Stewardship for AI: Privacy & Quality
Professional Certification Program
🎥
FormatLive + Recorded
📅
Duration8 Weeks
📜
CertificationVerified
Module 3
Program Objectives
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To ensure AI systems are trained and tested on reliable, secure, and fair data
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To bridge the gap between data science and data governance
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To empower organizations to deploy ethical, compliant AI at scale
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To reduce risks from poor data management in AI projects
Module 4
Program Structure
Module 5
Module 1: Principles and Roles in AI Data Stewardship
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Chapter 1.1: What is Data Stewardship and Why It Matters for AI
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Chapter 1.2: Responsibilities of Data Stewards in AI Projects
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Chapter 1.3: Data as a Strategic Asset – Ethics and Governance
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Chapter 1.4: Overview of AI Data Workflows: Collection, Curation, Use
Module 6
Module 2: Privacy-Centric Data Design
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Chapter 2.1: Understanding Data Privacy in the Context of AI
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Chapter 2.2: Legal Frameworks: GDPR, CCPA, HIPAA, and Global Laws
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Chapter 2.3: Personally Identifiable Information (PII) and Sensitive Data
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Chapter 2.4: Consent, Anonymization, and Data Minimization
Module 7
Module 3: Data Quality Dimensions and Standards
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Chapter 3.1: Defining Quality in AI Datasets (Accuracy, Completeness, Consistency)
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Chapter 3.2: Common Sources of Bias and Error
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Chapter 3.3: Tools for Validating and Profiling AI Data
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Chapter 3.4: Labeling Guidelines and Quality Control in Annotation Workflows
Module 8
Module 4: Data Lineage and Metadata Management
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Chapter 4.1: Why Data Lineage Matters in AI
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Chapter 4.2: Documenting Data Flow from Source to Model
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Chapter 4.3: Metadata Standards (DCAT, Schema.org, ISO 11179)
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Chapter 4.4: Creating and Maintaining a Data Catalog for AI Systems
Module 9
Module 5: Governance and Risk in AI Data Lifecycle
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Chapter 5.1: Building Governance Frameworks for AI Data
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Chapter 5.2: Conducting Data Risk Assessments
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Chapter 5.3: Auditing AI Data Pipelines for Compliance
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Chapter 5.4: Cross-Functional Collaboration with Legal, Security, and Data Teams
Module 10
Module 6: Future-Proof Stewardship and Capstone
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Chapter 6.1: Designing Scalable Stewardship Processes
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Chapter 6.2: Monitoring for Drift, Privacy Breaches, and Integrity Loss
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Chapter 6.3: Responsible Data Offboarding and Retention Strategies
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Chapter 6.4: Capstone Project – Design a Stewardship Plan for a Real AI Use Case
Module 11
Video Module TOC
~Video content aligned with weekly modules
Module 12
Week 1 Videos
Theme: Foundations of AI Data Stewardship & Privacy
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What is Data Stewardship in the AI Context
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The Role of Data Stewards in AI-Driven Organizations
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Ethical Foundations of Data Collection and Usage
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Key Concepts in Privacy by Design
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Global Privacy Laws: GDPR, CCPA, HIPAA Overview
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Identifying and Handling PII in Datasets
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Consent Management and Anonymization Techniques
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Data Minimization Strategies for Responsible AI
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Week 1 Recap and Stewardship Role Assessment
Module 13
Week 2 Videos
Theme: Data Quality, Bias, and Lineage in AI Systems
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Dimensions of Data Quality for ML Models
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Profiling, Cleaning, and Validating AI Data
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Common Biases in Annotation and Labeling
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Best Practices in Data Labeling Workflows
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Measuring and Managing Label Quality
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Data Lineage 101: Tracing Input to Output
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Metadata Standards and Automation Tools
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Creating a Data Catalog for AI Pipelines
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Week 2 Hands-On: Dataset Profiling Exercise
Module 14
Week 3 Videos
Theme: Governance, Compliance, and Sustainable Stewardship
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Building a Governance Framework for AI Datasets
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Risk Assessment in AI Data Pipelines
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Auditing for Regulatory Compliance and Fair Use
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Collaboration Across Legal, Security, and Data Teams
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Monitoring Data Drift and Privacy Breaches
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Offboarding, Retention, and Sunset Policies
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Scalable Stewardship Processes for Large Teams
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Capstone Briefing: Drafting a Stewardship Plan
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Final Recap: Becoming a Responsible AI Data Leader
Module 15
Lecture 1 (Week 1)
Title: Privacy by Design: Embedding Compliance into AI Data Workflows
Duration: 60 minutes
Focus: Legal foundations of data privacy and operationalizing consent, PII handling, and anonymization
Guest: Data Privacy Officer / Legal Counsel for AI Compliance
Interactive: Live scenario walkthrough: building a compliant data intake process for a new AI product
Module 16
Lecture 2 (Week 2)
Title: Data Quality in the Real World: From Labels to Lineage
Duration: 75 minutes
Focus: Tackling labeling bias, inconsistencies, and documenting data lineage in complex AI workflows
Guest: Data Quality Lead / AI Dataset Program Manager
Interactive: Group audit of a sample dataset pipeline, including label review and lineage mapping
Module 17
Lecture 3 (Week 3)
Title: Building a Culture of Stewardship: AI Data Governance at Scale
Duration: 90 minutes
Focus: Aligning cross-functional stakeholders on risk, retention, and long-term data responsibility
Guest Panel: Chief Data Officer + Governance Strategist + MLOps Engineer
Interactive: Peer review session of capstone data stewardship plans with expert feedback and discussion
Module 18
Intended For
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Data analysts, data engineers, AI/ML developers
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Compliance officers, policy professionals, and data privacy consultants
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AI product managers and quality assurance teams
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Recommended: Familiarity with basic data and AI concepts
Module 19
Program Outcomes
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Build and maintain high-integrity, privacy-compliant datasets for AI
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Develop systems to monitor data quality and minimize bias
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Understand and implement regulatory frameworks in AI workflows
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Operationalize data stewardship practices across teams and systems
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Align data management with ethical AI principles
Module 21
Fee Structure
Fee: INR 21499 USD 249
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
List of CurrenciesModule 22
FOR QUERIES, FEEDBACK OR ASSISTANCE
Module 23
Key Takeaways
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Module 24
Future Career Prospects
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AI governance and compliance
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Data operations and engineering
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Responsible AI development
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Cross-functional leadership in AI/ML organizations
Module 25
Job Opportunities
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Data Steward for AI/ML Teams
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AI Governance Analyst
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Data Privacy Consultant (AI Systems)
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Responsible AI Project Manager
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Data Quality and Compliance Engineer
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Data Trust and Ethics Lead
Module 26
Enter the Hall of Fame!
Take your research to the next level!
Publication Opportunity
Potentially earn a place in our coveted Hall of Fame.
Centre of Excellence
Join the esteemed Centre of Excellence.
Networking and Learning
Network with industry leaders, access ongoing learning opportunities.
Hall of Fame
Get your groundbreaking work considered for publication in a prestigious Open Access Journal (worth ₹20,000/USD 1,000).
Achieve excellence and solidify your reputation among the elite!
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Module 27
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