Three self-contained visualisation projects.
Live site: https://ishelar.github.io/dataviz-projects/
| Project | Type | Stack | Live demo |
|---|---|---|---|
dance-analytics-dashboard/ |
Multi-view interactive dashboard | Python · Plotly Dash · Pandas · Docker | Live on Hugging Face Spaces |
minard-d3-visualizations/ |
Browser-only D3 charts (Minard scatter + Gapminder iterations) | HTML · D3 v7 | Minard scatter · Gapminder I · Gapminder II · Gapminder III |
visualisation-analysis/ |
Critique of three published visualisations and a design proposal | Markdown + images | viewable on GitHub |
Interactive Plotly Dash app exploring a global dance-styles dataset through linked parallel coordinates, Sankey flow, geo bubbles and time-period small multiples. Installable Python package with environment-driven config, /health + /ready endpoints, and a production Dockerfile. Deployed as a Docker Space on Hugging Face: ishelar-dance-analytics.hf.space.
Static D3 charts: a scatter recreation of the data behind Minard’s 1869 Napoleon march map, and three progressively richer Gapminder iterations. Pure HTML + JavaScript + CSV — no build step, no server.
Munzner-framework critiques of three published visualisations (“Happy People, Happy Planet?”, a Sample Superstore sales dashboard, and a Musk Tweets infographic) plus a written design proposal for the dance dataset.
dataviz-projects/
├── README.md
├── LICENSE
├── dance-analytics-dashboard/
├── minard-d3-visualizations/
└── visualisation-analysis/
Each subfolder has its own README with run / hosting / content details.
MIT © Saurabh Shelar