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🌍 COVID-19 Data Exploration using Microsoft SQL Server

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.

📊 Project Goals

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

🔍 Why I Built This Project

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:

  1. Applying SQL in a real-world context: Working with a complex and evolving dataset.

  2. Practicing analytical queries: Using aggregation functions, filters, ordering, and calculating key metrics such as death rates.

  3. Building a solid portfolio project: Demonstrating my ability to transform raw data into actionable insights.

  4. 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.

🧩 Tools and Technologies

  • Microsoft SQL Server
  • SQL Server Management Studio (SSMS)
  • CSV file (Source: Our World in Data)
  • Git for version control

✅ Conclusion and Learnings

🎓 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.