Evaluating AI Outputs for Data Professionals

As AI takes on more of the analytical work, the most valuable thing a data professional can do is accurately and efficiently evaluate what comes back. Output evaluation is a learnable skill, and it's what separates analysts who use AI well from those who don't. In this course, Mo Chen teaches a systematic framework for evaluating any AI output—whether it's yours, a colleague's, or a tool's—across the full range of error types and validity checks.


Learning objectives
Identify and categorize common types of AI errors in data analysis contexts.
Apply cross-verification techniques to fact-check AI-generated claims against source data.
Assess statistical and logical validity in AI outputs using a practical red-flag checklist.
Build a personal evaluation workflow tailored to the analyses you do most often.
Produce a confidence-rated evaluation scorecard for any AI analysis you need to act on.