📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Data Stewardship for AI Privacy and Quality, from foundations to a certified capstone project.
Outline
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.
Outline
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.
Outline
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.
Outline
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.
Outline
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.
Outline
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.
Earn government-registered certification in Data Stewardship for AI Privacy and Quality
e-Certificate and e-Marksheet issued on successful completion.
