I've spent my career working at startups where using the wrong data had real consequences.

I wrote the statistics that led to FDA approval of the first AI to predict breast cancer risk, ensured that scaling retail brands got demand forecasts that allowed them to actually plan for the future, and built AI tools for delivering better health care to patients.

The technical work was rarely the hard part. The hard part was helping non-technical leaders read what the data was actually saying.

I translated my experiences into data courses taught at three Top 50 universities. Now, I'm offering the same opportunity to learn data judgment to operators, generalists, and anyone who ever said, "I'm not a data person."

Data fluencey isn't about code or math; it's about judgment.