AIDU-DATA-205
Delivery Type: Live, instructor-led, Remote or In person
Prerequisite: AI Foundations for Professionals, Machine Learning for Professionals
This course provides professionals with a rigorous, non-technical foundation in data literacy specifically for AI-enabled systems and workflows. Rather than teaching generic data analysis or statistics, it explains how data functions as the primary driver of AI behavior, often more influential than algorithms themselves.
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. Data is treated as a system component, not a static asset.
The course emphasizes understanding data quality, labels, proxies, feedback loops, leakage, and lifecycle dynamics from the perspective of professionals who work with AI outputs, tools, and decisions. By the end, participants can interpret AI behavior through a data lens and recognize when data is unsuitable for AI-driven decision-making.
Core Topics:
The role of data in AI systems
Types of data used in AI
Data collection and sampling bias
Data representation and features
Data quality in practice
Historical data and embedded assumptions
Labels, targets, and proxies
Distribution shift and data drift
Feedback loops and data reuse
Data leakage and contamination
Interpreting AI outputs through data
When data is the limiting factor
Outcomes:
Understand how data drives AI system behavior
Recognize different types of data used in AI systems
Identify common data quality and representation issues
Understand how labels, proxies, and assumptions affect outcomes
Recognize data leakage, feedback loops, and silent reuse
Interpret AI outputs in light of data limitations
Ask informed questions about data used in AI-enabled tools
Recognize when data is unsuitable for AI-driven decisions
