Financial data visualization
Adjunct Professor of Finance
University of Cincinnati
Graduate instruction on how a financial question becomes a model and a visual someone else can audit: assumptions, structure, and which input moves the result.
Teaching
Understanding is a deliverable.
I teach graduate financial data visualization at the University of Cincinnati as an Adjunct Professor of Finance. The same job shows up outside the classroom: leaving a client, an executive, or another analyst with a model they can use when I am not in the room.
How a class is built
- Start from the decision or the question, not from the menu of the tool.
- Name the definition before the visual. A chart of an unstable measure is a faster way to be wrong.
- Show the failure mode. Students should see how a correct calculation still supports a bad conclusion.
- Offer more than one depth. A first explanation that is honest can point to the mechanism without requiring it.
- Do not perform certainty the material does not have.
Courses
Data visualization with Power BI
Adjunct Professor of Finance
University of Cincinnati
Modeling and visual argument in one environment, so the chart stays attached to grain, relationships, and measure logic.
Course numbers and classroom materials are not republished here. Mentoring load is about thirty students per semester, per the resume.
Subject areas
- Financial modeling
- Power BI
- Data visualization
- Analytical reasoning
- Technical explanation
Public examples of the same pattern
- What a measure needs, A short progression from a number on a page to a definition someone can defend.
- From prompt to token, The same teaching pattern applied to language-model inference: overview, walkthrough, mechanism.
- Compression stack, Evidence, analysis, finding, decision, executive summary, one teaching example, labeled as a teaching example.