Data Governance, Quality, Privacy & Analytics Ethics is a structured track of 11 lessons that build a complete, interview-ready understanding of the topic. Work through them in order, then use the quiz and flashcards in each lesson to revise.
What this course covers
- Why Governance Exists: Trust as the Real Deliverable - What governance is actually protecting, and what happens to analytics without it.
- Data Ownership, Stewardship & Definitions That Hold - Who owns a metric definition, and how disagreements get settled in practice.
- Data Quality Dimensions and How to Measure Them - Completeness, accuracy, consistency, timeliness and validity, each made measurable.
- Data Lineage, Cataloguing & Documentation - Tracing a number back to source, and documenting so the next analyst can too.
- Master Data Management & Reconciling Conflicting Sources - When two systems disagree about the same customer, and how that gets resolved.
- Access Control, Anonymisation & Handling Sensitive Fields - Masking, aggregation thresholds and role-based access applied to analytics data.
- India's Data Protection Law: What Analysts Must Design For - The obligations the Digital Personal Data Protection Act places on analytics work.
- Consent, Purpose Limitation & Data Retention in Practice - Designing analysis that stays inside the purpose the data was collected for.
- Bias, Proxy Discrimination & Fairness in Models - How a model discriminates without using a protected attribute, and how to test for it.
- Explainability & Accountability for Automated Decisions - What must be explainable when a model affects a person, and who answers for it.
- Case Study: An Analytics Project Reviewed for Privacy and Ethics - A real-shaped project taken through a full privacy and fairness review.