A data analysis and visualization project tracking global COVID-19 trends using real-world data and Python tools.
This project explores global COVID-19 data to analyze cases, deaths, recoveries, and vaccination progress over time and across countries. The goal is to clean, analyze, and visualize COVID-19 trends, then present insights in a clear and informative Jupyter Notebook.
By the end of the project, you'll have an analysis report with charts and written summaries—suitable for presentation or publishing.
- ✅ Import and clean COVID-19 global data
- ✅ Analyze time trends (cases, deaths, vaccinations)
- ✅ Compare metrics across countries or regions
- ✅ Visualize trends with charts
- ✅ Communicate findings using narrative and code
Goal: Obtain a reliable COVID-19 dataset.
- Source: Our World in Data - owid-covid-data.csv
- Alternative: Johns Hopkins CSSE GitHub repo (advanced)
Action: Download owid-covid-data.csv and place it in your working directory.
Goal: Load and explore dataset structure.
Tasks:
- Load using
pandas.read_csv() - Preview columns and rows
- Check for missing values
Key Columns:
date,location,total_cases,new_cases,total_deaths,total_vaccinations, etc.
Goal: Prepare the dataset for analysis.
Tasks:
- Filter for countries of interest (e.g., Kenya, USA, India)
- Drop irrelevant/missing rows
- Convert
dateto datetime - Fill or interpolate missing numeric values
Goal: Generate trends and summaries.
Tasks:
- Plot total cases/deaths over time
- Compare daily new cases
- Calculate and analyze death rates
Visuals:
- Line charts
- Bar charts
- (Optional) Heatmaps for correlation
Goal: Track vaccination rollout across countries.
Tasks:
- Plot cumulative vaccinations
- Compare vaccination rates
Charts:
- Line charts
- Optional: Pie charts
Skipped in this project, but you can use Plotly or GeoPandas for geographic visualizations of global cases.
Goal: Summarize key findings.
Tasks:
- Document 3–5 narrative insights
- Highlight unusual trends or patterns
- Use markdown cells for storytelling
Deliverables:
- 📘 Jupyter Notebook (.ipynb)
- 📊 Visualizations
- 📝 Written summaries
pandasmatplotlibseabornJupyter Notebook
(Note: plotly or geopandas not used in this version.)