This project marks my first official step into health data science. I leveraged SQL to explore and analyze global child mortality trends using real-world data from Our World in Data.
Dataset
- Source: Our World in Data - Child Mortality
- Format: CSV
- Fields: ‘entity’, ‘code’, ‘year’, ‘mortality_rate’
Objectives
- Analyze historical and recent child mortality trends globally
- Identify highest and lowest performing countries by year
- Compare rates across regions and income groups
- Practice SQL querying and data analysis fundamentals
Tools Used
- MySQL Workbench
- Visual Studio Code (for cleaning)
- SQL
Key Insights
- Global child mortality has significantly declined since the 1960s
- African countries still report the highest rates in recent years
- Income level is a major determinant of child survival
- Decline in child mortality rate in Nigeria from 1964
Next Steps
- Visualize results with Tableau, Power BI, Python (Matplotlib, Seaborn)
- Explore additional health metrics
- Build predictive models
Author
This project was completed by a transitioning pharmacist turned aspiring health data scientist. Connect with me:
- LinkedIn : [https://www.linkedin.com/in/oluwanifemi-fakorede-b4a789248?)utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=ios_app]
- Medium : [https://medium.com/@Specialwrites]
Every dataset tells a story. This one tells of children who lived and those who did not. Let's keep asking better questions.
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