Healthcare SQL My Data Journey 2026 | Student Project 🧠 Project Context (In My Words) For this project, I’m stepping into the role of a Data Analyst working with healthcare data.

The scenario: MedCore Analytics supports St. Aurelius General Hospital, and the Finance team needs a clean raw data extract that combines:

Patient records Appointment history Billing information All of this lives in a PostgreSQL database.

My job is to:

Connect to the Supabase database Write structured SQL queries inside the healthcare schema Join the relevant tables Generate a clean flat file → raw-data.csv Prepare it for downstream ETL work This project is about practicing real-world SQL, joins, structure, and reproducibility — not just writing queries, but building something complete and usable.

🏥 Database Details Schema: healthcare Core Tables I’m Working With:

patients appointments billing doctors departments Final Deliverable: data/raw-data.csv — a joined dataset combining patient, appointment, and billing data.

• What Success Looks Like (For Me) I can confidently run queries in DBeaver against the healthcare schema My SQL covers: Basic SELECT statements Aggregations Joins At least one CTE or window function python run.py connects to the database successfully The script generates data/raw-data.csv The CSV contains fields from multiple joined tables The project is cleanly structured and pushed to GitHub
• How I’m Structuring This I’m building this from scratch, using the teaching project only as a guide.

Project structure:

text

project-root/ │ ├── sql/ # All SQL queries ├── data/ # Output CSV ├── run.py # Database connection + export script └── README.md Why This Project Matters To Me This isn’t just a SQL exercise.

It’s me practicing:

Writing production-style SQL Structuring a real data project Connecting Python to PostgreSQL Exporting clean datasets Thinking like an analyst, not just a student