A self-directed Tableau project exploring New York City's short-term rental landscape through interactive filtering, geographic views, and pricing analysis across all five boroughs — built purely to sharpen dashboard design skills.
This project exists entirely outside my professional work — and that's intentional. Personal projects give me a space to practice design decisions I don't always control at work: what chart types to use, how to layer information, how much to show upfront versus hide behind a filter.
I chose the AirBnB NYC dataset because it's rich, realistic, and multi-dimensional — geography, room type, price, availability, host behavior, and review frequency all create genuine analytical tension without needing to manufacture complexity.
The process involved shaping and cleaning data in Tableau Prep, then rebuilding the dashboard layout through several iterations. I paid close attention to how filters cascade across sheets, how to use color to encode meaning rather than decoration, and how to keep the top-level view scannable while preserving access to detail.
The goal was never a polished deliverable — it was deliberate, honest practice. Every version of this dashboard taught something the previous one didn't.
| Viz | Tableau Desktop, Tableau Public |
| Prep | Tableau Prep Builder |
| Dataset | Inside AirBnB — NYC open listings data |
| Charts | Maps, bar, scatter, treemap, KPI tiles |
| Techniques | LOD expressions, dashboard actions, calculated fields, parameter controls |
Back to the portfolio for research, ML, and industry projects.