Finding My Path: From Computer Science to Urban Data Science

May 23, 2026 • Reflections & Urban Data • 4 Min Read

I recently finished what will likely be my last semester as an undergraduate student. I took a class called ‘Projects in Data Science’ that really opened my eyes to the world of data science and engineering.

I realized through this class that I can apply my Computer Science degree to a myriad of topics, so long as I use data as the middleman. When it came time for my semester capstone project (a 3-week project covering all of the topics we went through this last semester), I knew exactly what I’d do.

The Pivot

I started my undergraduate degree at 16, graduating high school with my A.A. degree. Throughout that time, I had the distinct goal of being an architect, applying to college as an architecture major and getting a full ride as such. I had chosen architecture because of my interest in designing the world that people live in.

However, when I learned that UCF’s school of architecture was 45 minutes away from the apartment I had leased, I had to pivot. I chose Civil Engineering as my replacement major and enrolled in classes accordingly. After a semester from hell, I once again pivoted, opting for the easy road of Computer Science (this was pre-AI, and C.S. still looked like a very straightforward path to success), jumping at the opportunity to go fully online and live in the glamorous South Florida.

What I mean to say with all this is that my dreams of working on the world around me were pretty much crushed; I had resigned myself to working on apps. Up until six months ago, that meant cloud engineering, as I thought it sounded interesting (and it is!). This class, and importantly this project, showed me perhaps a path back to where it all began.

The Capstone Project

The project: Answering a question with data. Simple enough. I did not give much thought to the primary questions, as there wasn’t really a requirement other than it being a question that could be solved with data, which covers nearly all questions.

The Core Questions

These were my questions—very easy to answer with just a couple of public databases. I went looking for databases before even establishing the questions and came across some cool Canadian databases on pedestrian and cycling infrastructure.

  • Question 1: "Does the presence of high-tier, separated infrastructure (Cycle Tracks and Pedestrian Zones) directly correlate with higher 15-minute accessibility scores for essential health and food services?"
  • Question 2: "How do path dimensions (width and length) and materials differ between cities with 'Extra High' accessibility versus those with 'Below Average' accessibility?"

Rediscovering Architecture Through Data

While doing this project, I remembered why I started in architecture. It was really nice thinking that there was a correlation between the effort from those in charge of public infrastructure and people’s actual ability to get to the places they need to go for free.

I’ve kept up with architecture and urban planning through YouTube, treating it as background noise and something to do during breaks from homework, but it never felt like I was able to interact with that world meaningfully until I answered those two simple questions.

Looking Forward

I think I’m going to go to graduate school as soon as I have the means, and I’ll major in Urban Data Science. For now, I’ll be working on similar projects, ones with questions that maybe aren’t so clear-cut.

Below I'll link the paper that I turned in for this project, but a lot of what I did was just to fulfill requirements and is NOT what I’d actually do to get legitimate answers. I did however learn a lot about data science reporting from writing this paper.

Link to Report Paper

I’m actually working on a very interesting tool to make projects like this much easier. There is a prototype for this tool available, look at my Projects Page for a link to it.