Build a Public Open-Data Dashboard for Urban Mobility
Overview
What this challenge is about.
Pull the city's open-data cyclist-collision dataset (10 years of incidents, geocoded). Define a clear before/after window around the protected-lane rollout, control for traffic-volume change using the city's count-station data, and surface 4-5 charts that show injury-rate change with appropriate uncertainty. Publish the dashboard on Streamlit Community Cloud or similar and write a methods note covering data sources, cleaning choices, statistical caveats, and what the analysis cannot conclude. Success: a journalist could quote the dashboard accurately.
The Brief
What you'll do, and what you'll demonstrate.
Publish a defensible, interactive dashboard on cyclist-injury-rate change after a protected-lane rollout, with a methods note that survives political scrutiny.
Earning criteria — what you'll demonstrate
- Work end-to-end with messy open civic data
- Apply before/after analysis with appropriate confounders
- Communicate uncertainty visually for a public audience
- Defend analytical choices in writing to a non-technical board
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Career paths this builds toward
Canonical rolesData Scientist
Public civic-data work with a defensible methods note is a portfolio piece any junior data scientist can point to in interviews to prove communication chops.
This challenge sharpens
- exploratory-data-analysis
- data-storytelling
- geospatial-analysis
Data Engineer
Wrangling messy open data into a snapshotted, versioned pipeline is exactly how data engineers operationalize public-data work.
This challenge sharpens
- data-wrangling
- python
- geospatial-analysis
AI Product Designer
Designing an honest interactive dashboard for a public audience is a transferable craft for designing data-driven product surfaces.
This challenge sharpens
- data-visualization
- data-storytelling
- exploratory-data-analysis