Objective Explore global COVID-19 trends by analyzing cases, deaths, and vaccination data using SQL to uncover patterns and insights across countries and continents. Focus on data cleaning, integration, and analytical SQL techniques to extract key metrics.
Specifications
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Data Sources:
CovidDeaths (3).xlsx– cleaned dataset containing global COVID-19 cases and deaths.CovidVaccinations.xlsx– cleaned dataset containing global vaccination data.
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Tools and Technologies: SQL, Python, Jupyter Notebook, Pandas, Excel, Tableau.
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Core Skills and Concepts: Data cleaning, data transformation, joins, aggregation, filtering, window functions, CTEs, temporary tables, and views.
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Key Analyses:
- Total cases vs. total deaths — measures the likelihood of death upon infection.
- Total cases vs. population — determines the infection rate by country.
- Countries with the highest infection rates relative to population.
- Countries and continents with the highest death counts.
- Global summary of cases, deaths, and vaccinations.
- Total population vs. vaccinations — estimates vaccination coverage worldwide.
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Deliverables:
- SQL queries and results visualization in Jupyter Notebook.
- Analytical insights highlighting COVID-19 trends and vaccination progress.
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Technical Practice: Demonstrates proficiency in SQL scripting, data manipulation, and exploratory data analysis (EDA).
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File:
SQL_Project.ipynb— contains full workflow from data import to analysis and visualization.