Thembelitchi/digital-health-africa-applied-datascience

Applied data science workflows, clinical validation pipelines, and product analytics for digital health systems in global health contexts. Built for the Digital Health Mentorship Program # 🏥 Applied Data Science & Analytics for Digital Health Systems A curated repository of end-to-end, interactive Jupyter notebooks for digital health engineers, clinical researchers, and health tech product managers. Developed for the **Digital Health Mentorship Program 2026**, this repository covers data pipeline design for messy health records, clinical AI model validation across distinct hospital populations, and data-driven product prioritisation. --- ## 📂 Repository Layout ```text . ├── Project_1_Data_Pipelines.ipynb # Ingestion, Data Hygiene & Facility Reference Joins ├── Project_2_Clinical_Validation.ipynb # Internal/External Model Validation & Translational Gap ├── Project_3_Product_Analytics.ipynb # Catalog Analytics & Priority Score Roadmap Matrix └── README.md # Master Repository Documentation ``` ## 🚀 Projects Overview | Notebook File | Applied Domain | Core Problem Addressed | Key Tools & Libraries | Interactive Colab | | :--- | :--- | :--- | :--- | :--- | | **Project_1_Ali_Data_Pipelines.ipynb** | **Data Engineering** | Ingesting messy clinical visit logs, standardising text/dates, and joining master facility registries. | pandas, matplotlib, seaborn | Open In Colab | | **Project_2_Vincent_Clinical_Validation.ipynb** | **Clinical AI / ML** | Measuring the *Translational Validation Gap* when deploying risk prediction models across hospitals. | scikit-learn, ucimlrepo, seaborn | Open In Colab | | **Project_3_Rabbi_Product_Analytics.ipynb** | **Product Analytics** | Prioritising digital pharmacy features using catalog volume and price dispersion matrices. | pandas, seaborn, matplotlib | Open In Colab | ## 🔍 Comprehensive Project Breakdowns ### 📘 Project 1: Designing Data Pipelines for Messy Health Data * **Clinical & Systems Context:** In resource-constrained health systems, patient records arrive from fragmented sources (mobile apps, paper clinic registries, remote monitoring devices). These files frequently c …