// case study · SQL analysis

Decoding COVID-19 Impact

A pure-SQL exploration of global COVID-19 data — using window functions, CTEs, and aggregation to surface global infection trends, country-level severity, and population-vaccination relationships.

SQL Server Window Functions CTEs Data Aggregation
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Overview

This project treats the pandemic's data trail as a SQL playground — no dashboards, no BI tool, just well-structured queries that answer real questions: which countries bore the heaviest relative impact, how vaccinations tracked against population, and where the per-capita infection rates hit hardest.

Techniques

Window Functions Common Table Expressions Table Joins Aggregation & Filtering

Key Findings

  • Created window functions to uncover global trends in infection and death rates.
  • Calculated location-specific death percentages for severity assessment through aggregation and filtering.
  • Identified countries with the highest infection rates relative to population via comparative analysis.
  • Analyzed population-vaccination rate relationships using Common Table Expressions (CTEs).
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