An end-to-end analytics project combining Python-based Exploratory Data Analysis (EDA) with an interactive Tableau dashboard to track global + India COVID-19 spread, compare severity, and visualize country-wise impact.
This project analyzes COVID-19 case and vaccination data to generate actionable insights through:
- Python EDA for cleaning, structuring, and trend exploration
- Tableau dashboards for interactive, decision-ready visual storytelling
- Top 10 Confirmed Countries (ranking by total confirmed cases)
- Global Cases Map (geographic spread visualization)
- Top 10 Deaths (Treemap) (high-mortality countries)
- Top 10 Recoveries (Bubble Chart) (recovery comparison across countries)
- Confirmed vs Deaths (Scatter Plot) (severity vs volume relationship)
- Impact via data cleaning | transformed raw case + vaccine datasets into analysis-ready tables | improved reliability of insights
- Impact via EDA | analyzed country-wise confirmed/deaths/recoveries trends | identified high-burden regions faster
- Impact via dashboarding | built Tableau visuals (map, treemap, scatter, ranking charts) | enabled quick comparison across countries
- Impact via severity mapping | plotted confirmed vs deaths to highlight risk clusters | simplified prioritization for monitoring
- Python (EDA)
- Tableau (Dashboarding + Visualization)
- Data Cleaning + Formatting
- Univariate/Bivariate Analysis
- CSV Processing
Covid 19 Analysis.ipynb— Python EDA notebookCovid 19 Analysis.twb— Tableau workbookCovid 19 Analysis Dashboard.pdf— Dashboard exportcovid_19_india.csv— India COVID datasetcovid_vaccine_statewise.csv— Vaccination dataset
- Open
Covid 19 Analysis.ipynband run the notebook to reproduce EDA outputs - Open
Covid 19 Analysis.twbin Tableau and refresh data sources - View
Covid 19 Analysis Dashboard.pdffor the final dashboard snapshot
A clean Tableau dashboard delivering:
- Global COVID snapshot
- Top 10 country rankings (confirmed/deaths/recoveries)
- Confirmed vs deaths severity mapping
- Geographic spread visualization