This project aims to perform a data exploration analysis using Microsoft SQL Server with public data from the Our World in Data project, which tracks the global impact of the COVID-19 pandemic.
Explore and analyze global COVID-19 data to identify meaningful patterns, trends, and actionable insights such as:
- Total number of confirmed cases and deaths by country and by continent
- Mortality rate (total deaths per confirmed cases) by country and region
- Time-based evolution of confirmed cases, deaths, and vaccinations
- Percentage of the population infected and vaccinated per country
- Relationship between vaccination rates, case numbers, and mortality
- Comparative analysis of the most affected countries
- Data consolidation using Views, Temporary Tables, and CTEs for future visualizations and reporting
The main motivation behind this project was to deepen my SQL skills by working with real and relevant data. I used publicly available COVID-19 data from Our World in Data to perform meaningful and practical data exploration.
🎯 My key objectives included:
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Applying SQL in a real-world context: Working with a complex and evolving dataset.
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Practicing analytical queries: Using aggregation functions, filters, ordering, and calculating key metrics such as death rates.
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Building a solid portfolio project: Demonstrating my ability to transform raw data into actionable insights.
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Contributing to the community: Sharing a practical example that others can study, adapt, and expand upon.
This project also gave me the opportunity to work with Microsoft SQL Server in a hands-on scenario, strengthening both my technical and analytical skills.
- Microsoft SQL Server
- SQL Server Management Studio (SSMS)
- CSV file (Source: Our World in Data)
- Git for version control
🎓 By completing this project, I gained several valuable insights:
Mastery of SQL for exploratory analysis: I improved my ability to write complex queries, including joins, subqueries, and aggregate functions.
Understanding of public health data: I developed a better sense of how to analyze and interpret global health datasets.
Importance of data preparation: I recognized the critical role of data cleaning and transformation for accurate analysis.
Communicating insights effectively: I enhanced my ability to clearly and concisely communicate findings—an essential skill for data-driven decision making.
This project reinforced my passion for turning data into meaningful information and highlighted the importance of a thoughtful, data-driven approach to solving real-world problems.