Ethan Pitzer | Data Science & Analytics Engineering

Welcome to my data science lab! This repository hosts my data science case studies, automated data pipelines, and interactive dashboards.

Visit the Live Portfolio

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Repository Structure

This site is architected to separate deep-dive engineering work from visual analytics:

• projects/ (Case Studies): Full-stack data engineering projects with Python pipelines, automated testing, and written analysis.
• dashboards/ (Visualizations): Hosted Tableau/PowerBI embeds and interactive Plotly apps.
• projects/oregon-fqhc/tests/: Automated pytest suites ensuring data integrity before deployment.

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Featured Case Study: Oregon FQHC Landscape

A reproducible annual snapshot of Oregon's HRSA-reported health-center landscape. It combines a dated site-footprint extract with organization-level 2024 UDS measures, identifier-based joins, validation, and transparent analytical reporting.

The Architecture

This project demonstrates a scheduled ETL pipeline with explicit validation gates running entirely on GitHub Actions:

1. Ingest: Python scripts retrieve authoritative, separately dated source snapshots from the HRSA Data Warehouse and FOIA reading rooms (2024 UDS patient measures). These are not live EHR counts.
2. Transform: pandas performs cleaning, geospatial field standardization, and validated many-to-one joins. Current coverage and precise counting definitions are documented in the project data contract.
3. Validate: pytest separates hard integrity failures from visible reconciliation warnings and analytical-review triggers.
4. Analyze and publish: One equally weighted row per organization supports an exploratory log-scale size/Medicaid-share model, and Quarto renders the static case study only from validated artifacts.

View the Full Case Study

Featured Project: Clinic Service Forecaster

Goal: Predict future patient visit volumes to optimize staffing and budgeting for a community health center.

• The Challenge: Real patient data is HIPAA-restricted. I needed a way to demonstrate advanced forecasting capabilities without compromising privacy.
• The Solution:
  • Synthetic Data Engineering: Wrote Python scripts to generate 5 years of daily clinic data, incorporating realistic seasonality, weekly cycles, and "structural breaks" (e.g., COVID-19 lockdowns).
  • Time Series Modeling: Decomposed the data using Statsmodels to isolate trends and applied a SARIMA (Seasonal AutoRegressive Integrated Moving Average) model for 52-week forward predictions.
  • Business Impact: Translated model outputs into actionable strategies for dynamic staffing and resilience planning.
• Tech Stack: Python, Pandas, Statsmodels, Scikit-Learn, Seaborn, Quarto.

View the Full Case Study & Forecast

Global Tech Stack

• Languages: Python, R, SQL
• Data Engineering: Pandas, Requests, Pytest, GitHub Actions
• Visualization: Plotly, Seaborn, Tableau
• Modeling: Statsmodels, Scikit-Learn
• Analysis: Time Series Decomposition, Forecasting
• Reporting: Quarto