COVID-19: A Retrospective

Overview

This repository contains a retrospective analysis of the COVID-19 pandemic, aiming to reflect on the global response, impact, and lessons learned. The goal is to assess data, trends, and events that shaped the pandemic's course, and to provide insights for future preparedness.

Table of Contents

• Project Purpose
• Objectives
• Data Sources
• Scope
• Assumptions and Contraints
• License

Project Purpose

To analyze the rapid rise and fall of the COVID-19 pandemic and present the insights through an interactive dashboard. The project aims to provide a simple retrospective of the pandemic, leveraging data from its inception to its rapid eventual decline.

Objectives

• Collect, clean, and store COVID-19-related data from reliable sources.

• Perform data exploration and transformation using Python, Excel, and MySQL.

• Create interactive and insightful visualizations using Tableau and Power BI.

• Provide actionable insights on the pandemic’s trends, peak periods, and decline.

• Deliver a fully functional dashboard

Scope

• In-Scope:

  • Collection of COVID-19 data, including cases, deaths, tests and vaccination rates.

  • Data processing using Python, Excel, and MySQL for ETL (Extract, Transform, Load).

  • Development of data visualizations using Tableau and Power BI.

  • Deployment of a dashboard accessible to stakeholders.

• Out-of-Scope

  • Predictive modeling or forecasting of future pandemics.

  • Integration with live or real-time data feeds.

Assumptions and Constraints

• Assumptions:

  • Data sources will provide reliable and complete COVID-19 datasets.

  • The tools used (Python, Excel, MySQL, Tableau, Power BI) are sufficient for the project’s scope.

  • Stakeholders will provide timely feedback during reviews.

• Constraints:

  • Limited budget for software licenses and infrastructure.

  • Dependence on historical data completeness and accuracy.

Risk Overview

1. Data Quality Risks: Incomplete or inconsistent datasets may affect analysis.

• Mitigation: Use data validation techniques and reliable sources.

2. Technical Risks: Challenges in integrating tools (e.g., MySQL to Tableau).

• Mitigation: Conduct testing and ensure technical compatibility. Review available resources and workarounds

3. Timeline Risks: Delays in data preparation or dashboard development.

• Mitigation: Set buffer times and conduct regular progress reviews.

Sources

• https://ourworldindata.org/
• https://en.wikipedia.org/
• https://www.google.com/

License

This project is licensed under the MIT License - see the LICENSE.md file for details.