Teaching
Financial modeling and Power BI, taught
Adjunct teaching at the University of Cincinnati: graduate financial data visualization with Power BI, aimed at fluency rather than a tour of features.
- QuestionA model that only its author can explain is a fragile operational asset.
- RoleAdjunct Professor of Finance. Design the progression from a measure's meaning to the model, the visual, and the way the visual can mislead.
- What I didUnderstanding is a deliverable. Each layer of the model has to be something a student can reconstruct.
- DemonstratesInstructional design and the same translation skill used with clients, pointed at learners.
The question
Analytics education often starts with the interface. Students can click to a chart and still not know what the measure includes, what grain it is at, or what decision it is allowed to support. The model then lives or dies with whoever built it.
The situation
I teach as an Adjunct Professor of Finance at the University of Cincinnati (Aug 2024 to Present), mentoring about thirty students per semester. The public subject is graduate financial data visualization. Course numbers and syllabi are not republished here without a deliberate pass.
The system
A learner, a financial question, a model structure, a semantic or reporting layer in Power BI, and a visual argument. The system includes the ways a correct query can still support a bad decision, wrong grain, mixed definitions, a visual that implies a trend the data does not carry.
My role
I am responsible for the learning progression: what students should be able to explain, not only what they should be able to click. I teach financial data visualization. I do not describe myself as the students' employer or as the owner of their later work.
What I did
Move in an order that builds a usable mental model. What is being counted. What is deliberately left out. How the relationships are structured. What the visual is claiming. Where that claim can fail. Feature demonstrations sit inside that order instead of replacing it.
The artifact
Instruction in financial data visualization with Power BI. Public examples of the same teaching stance are the measure explainer and the prompt-to-token walkthrough on this site. Classroom decks are not posted here.
What changed, and what was learned
No grade distributions, enrollment figures, or before-and-after skill scores are stated. I will not invent them.
What this demonstrates
That explanation is a professional output. The same muscle, compress, then offer a path into the mechanism, is what executive communication and technical writing both ask for.