DataHacks 2026 Registration Analytics Dashboard

Static browser dashboard for monitoring live DataHacks 2026 registration data from published Google Sheets CSV exports.

What it shows

• Topline registration metrics: total registrations, progress toward the registration goal, universities represented, and first-time hackers
• Participant breakdowns for tracks, majors, minors, level of study, university, referrals, first hackathon status, shirt sizes, overnight stay, food restrictions, interests, biotech focus, activities, and club memberships
• Skill distribution snapshots for coding/data, embedded systems, UI/UX and web, and product/presentations
• Separate mentor/judge analytics for company, role category, participation type, and UCSD affiliation

How it works

• The app fetches two published CSV feeds directly in the browser:
  • participant registrations
  • mentor/judge registrations
• Data is normalized client-side in app.js
• The dashboard refreshes automatically every 60 seconds and also supports manual refresh from the UI
• No build step, framework, or backend is required

Run locally

Serve the project over HTTP so the browser can fetch the CSV sources:

python3 -m http.server 8000

Then open http://localhost:8000.

Do not open index.html directly as a file:// page. Browser fetches to the published sheets are expected to run from a local web server.

Configuration

Update these constants in app.js to point at different sheets or targets:

• CSV_URL: participant registration CSV
• MENTOR_CSV_URL: mentor/judge registration CSV
• REGISTRATION_GOAL: topline registration target
• REFRESH_INTERVAL_MS: polling interval

Project files

• index.html: dashboard layout and chart containers
• styles.css: visual design and responsive layout
• app.js: CSV parsing, normalization, metric aggregation, and chart rendering

Notes

• The dashboard expects the Google Sheets to remain published as CSV
• Graduation year is not displayed because it is not present in the participant sheet schema
• Several free-response fields are grouped into normalized categories before rendering so the charts stay readable