Austria Tourism Dashboard: Seasonal Overnight-Stay Analysis
Combined the provided tourism CSV tables, calculated seasonal aggregates, and produced a one-page HTML dashboard and written analysis report.
Case Snapshot
Role
Platform ownership: frontend implementation, deployment workflow, content structure, CI validation, and production release path.
Scope
I built a barebones but performant Python pipeline that loads and combines the provided CSV files, derives seasonal aggregates, prepares summary metrics, exports dashboard-ready data, and renders a polished one-page HTML report...
Constraints
The dashboard uses public statistical data and static generated artifacts, so it avoids database credentials, live APIs, and user data collection. The pipeline writes combined CSV files, summary JSON, dashboard HTML, widget...
Result
Combined the provided tourism CSV tables, calculated seasonal aggregates, and produced a one-page HTML dashboard and written analysis report.
Architecture
Data sources
Semicolon-separated CSV tables from Austrian accommodation statistics provide overnight stays by country of origin and federal states.
Processing
Python combines the files, cleans fields, computes seasonal totals, ranks countries and regions, and exports summary metrics.
Presentation
The final artifact is a static HTML dashboard and report that can be opened locally or hosted as a lightweight web page.
Data sources
Semicolon-separated CSV tables from Austrian accommodation statistics provide overnight stays by country of origin and federal states.
Processing
Python combines the files, cleans fields, computes seasonal totals, ranks countries and regions, and exports summary metrics.
Presentation
The final artifact is a static HTML dashboard and report that can be opened locally or hosted as a lightweight web page.
Architecture Views
Concise system views summarize the project boundary, deployment path, and data flow without adding implementation claims.
System overview diagram
Data sources
Deployment diagram
Processing
Data flow diagram
Presentation
Technical Decisions
- Combines multiple semicolon-separated tourism tables into one analysis dataset
- Ranks important origin countries and Austrian federal states
- Uses multiple chart types and text widgets to explain seasonal tourism patterns
- Generates dashboard HTML, submission report, metrics JSON, and image exports
- Runs with lightweight Python and static web output rather than a heavy BI stack
Challenges
- Raw tourism CSV tables are difficult to inspect directly and do not tell a clear story about seasonal importance, origin countries, or regional hotspots. The task required a concise...
- The dashboard uses public statistical data and static generated artifacts, so it avoids database credentials, live APIs, and user data collection.
- The pipeline writes combined CSV files, summary JSON, dashboard HTML, widget exports, and a submission report so the analysis can be reproduced and inspected from multiple artifacts.
Lessons Learned
- Combined the provided tourism CSV tables, calculated seasonal aggregates, and produced a one-page HTML dashboard and written analysis report.
- Generated dashboard HTML, metrics JSON, image exports, and a submission report from one Python workflow
- Kept the output static so the result can be opened locally without a database or BI server
Future Improvements
- Keep architecture views aligned with the implementation.
- Keep documentation concise: align README, architecture decisions, and screenshots.