Data Literacy for AI Systems

About this course

This course provides a rigorous, non-technical foundation in data literacy specifically for AI-enabled systems and workflows.

Rather than teaching generic data analysis or statistics, the course explains how data functions as the primary driver of AI behavior. Participants learn how data is collected, represented, transformed, and reused in AI systems, and why data-related assumptions are the most common source of failure, bias, and misinterpretation in real-world AI applications.

The course treats data as a system component, not a static asset. Participants examine how data choices affect learning, generalization, reliability, and downstream decisions, even when models appear sophisticated. Emphasis is placed on understanding data quality, proxies, labels, feedback loops, and lifecycle dynamics from the perspective of professionals who work with AI outputs, tools, and systems, not those who build models.

This course is designed for non-AI professionals who interact with AI systems through reports, dashboards, tools, documents, and decisions, and who need a clear, durable understanding of how data shapes AI outcomes.