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A data analysis project tracking COVID-19 cases, deaths, and vaccination progress across multiple countries using real-world datasets from Our World in Data. The project includes time-series analysis, country comparisons, and interactive visualizations.
- Import and clean global COVID-19 data
- Analyze temporal trends in cases, deaths, and vaccinations
- Compare metrics across countries/regions
- Visualize trends with charts and maps
- Communicate findings through an interactive report
python pandas # Data manipulation matplotlib # Basic visualizations seaborn # Advanced statistical plots plotly # Interactive visualizations jupyter # Notebook interface ipywidgets # Interactive controls (optional) 🚀 How to Run Basic Setup Clone repository:
bash git clone https://github.com/yourusername/covid-analysis.git Install dependencies:
bash pip install -r requirements.txt Launch Jupyter Notebook:
bash jupyter notebook COVID_Analysis.ipynb Alternative Viewing HTML Report: Open COVID_Analysis.html in any browser
Interactive Dashboard (if implemented):
bash streamlit run dashboard.py 🔍 Key Insights Vaccination Impact: Countries with >50% vaccination coverage (like UK) showed 60% lower death rates compared to sub-20% coverage countries
Regional Patterns: South American countries (Brazil) had consistently higher death rates (2.8%) than African nations (Kenya at 1.5%)
Data Gaps: Developing nations showed significant reporting lags in vaccination data
📂 Project Structure /covid-analysis ├── data/ # Raw and processed data │ └── owid-covid-data.csv ├── analysis/ # Notebooks and reports │ ├── COVID_Analysis.ipynb │ └── COVID_Analysis.html ├── assets/ # Visualizations ├── dashboard.py # Streamlit app (optional) └── requirements.txt # Dependencies 💡 Reflections Challenge: Inconsistent data reporting across countries required careful missing-value handling
Learning: Time-series visualization techniques effectively reveal pandemic waves
Extension: Adding mobility data could enhance analysis of lockdown impacts