Bess Hagan

· Planning Specialist · Computer Science Graduate ·

Data analysis, reporting automation, and permitting systems, with a background in Python, SQL, and applied research.

📊 About Me

Profile Picture

I am a Computer Science graduate from Southwestern University (December 2025), where I earned my degree Summa Cum Laude with a 4.0 GPA and a minor in Data Analytics. Before transferring, I completed an Associate of Science in Computer Science at Temple College. I now work as a Planning Specialist for the City of Georgetown Planning Department, where I administer permitting software, automate recurring reports, and analyze permitting data.

Through undergraduate research in the 2024 DREU and 2025 SURF programs, I built a SQL-to-Python data pipeline for behavioral log data and coauthored a peer-reviewed paper presented at AIIDE-25.

Together, these experiences shaped what I care about most: data people can trust. I like validating it, understanding its limits, and explaining what it shows to non-technical audiences. I'm interested in data analyst, permitting and planning data, and process improvement roles, especially in the public sector or govtech.

  • 🔍 Interests: data validation, reporting and automation, permitting and planning data, research analytics
  • 🛠 Tools: Excel (Pivot Tables, macros), SQL, Python, PostgreSQL, GitHub
  • 📚 Libraries: Pandas, NumPy, Matplotlib, scikit-learn

🔬 Projects

📊 Excel vs. Python: E-Commerce Sales Analysis

screenshot of top of DA project page

A comparative analysis of e-commerce sales data using Excel Pivot Tables and Python (Pandas + Matplotlib) to evaluate analytical workflows and business insights.

View Analysis

🍄 Mushroom Classification with KNN

KNN Result: Confusion Matrix (edible or poisonous)

Built a K-Nearest Neighbors classifier to predict whether mushrooms are edible or poisonous using a classic public dataset.

View the Colab Notebook

🐼 DREU 2024 Research

Research & Creative Works (RCW) poster

Built a SQL-to-Python data pipeline and applied network analysis to study student behavior in a Minecraft-based learning environment.

DREU Research Page

🧠 SURF 2025

Screenshot of Mario level generation Screenshot of Multiplayer Story Game

Trained and evaluated diffusion models to generate playable Mario levels and built a real-time multiplayer LLM Story Game using WebSockets.

SURF Research Page

🎮 Capstone: Senior Seminar in Software Engineering

Research & Creative Works (RCW) poster

Designed and co-developed Evergrove, an educational game in Godot to teach data science to children.

Download and play it on itch.io See repo

📬 Contact

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