Public Health Data Insights Dashboard
A Python data engineering and analytics tool for malaria epidemiology reports across African countries. The system provides data loading, validation, cleaning, analysis, export workflows and a structured CLI over a layered architecture.
- Dataset
- African malaria reports · 54 countries
- Stack
- Python · pandas · SQLite · Click
- Architecture
- Layered DDD-style design
- Testing
- 36 test files across services, domain and CLI
- Status
- Active · 2025
Overview
This project turns public-health malaria data into a reusable analysis tool. It supports loading, cleaning, validating, filtering, summarising and exporting epidemiology records, with persistent storage in SQLite and a command-line interface built with Click.
Architecture
Step 01
CSV data
African malaria dataset
Step 02
Cleaning
Validation · type conversion
Step 03
SQLite
Persistent records
Step 04
Services
Analysis · ingestion · export
Step 05
Domain layer
Entities · value objects
Step 06
CLI
Click commands
Step 07
Reports
Stats · filters · exports
What I built
- A Click-based CLI for loading, validating, analysing, filtering, viewing and exporting records.
- Data cleaning utilities for missing values, numeric conversion and validation reports.
- SQLite persistence with CRUD operations and repository-style data access.
- Analysis services for country-level statistics, high-risk countries, yearly trends and regional summaries.
- Domain entities and value objects for health indicators, case counts, incidence rates and prevention metrics.
- A test suite covering CLI commands, services, domain models, data access and utility behavior.
What it shows
This is useful evidence for data and software engineering roles because it goes beyond notebook analysis. The project has maintainable layers, persistent storage, validation, exports, logging and tests, which are the engineering foundations needed to turn an analysis workflow into a reusable tool.