This section is new.
And honestly, it's the one I'm most excited about.
I went back to school to finish my computer science degree. A lot of it has been a refresher — my experience put me ahead of the curve in enough areas that I've been able to move through coursework with more focus than I expected.
I was able to substitute a course with data science, and something clicked that I didn't anticipate. The code was second nature. Python for cleaning, organizing, sampling, visualizing — that part came easily. What it gave me was space to slow down and actually think about the statistics. Not just how to run the numbers but what the numbers mean, where they mislead, how to read them honestly.
I've been spending time with that side of it deliberately. Reading, listening, sitting with it. Currently working through The Data Detective by Tim Harford — a book about how statistics get misread and how to think more carefully about what data is actually telling you. It's been quietly changing how I approach the whole thing.
I work as an aide supporting individuals with autism, Down syndrome, and a range of developmental disabilities — people who are deep in the spectrum and require highly personalized, carefully considered support. My role involves collecting behavioral data, working through therapy goals, and being present when someone is in crisis.
It's meaningful work. And it generates a lot of information.
Right now that information lives at the individual level — data collected on each client to help build and refine their specific action plans. That's valuable. But I've been sitting with a question for a while:
What's in the pattern across all of it?
Across clients, classrooms, staff — there's data that isn't being looked at in aggregate. And I think there's something there. Not a hunch, not just an itch to do something data-related — a genuine belief that looking at this more carefully could produce something measurably useful for the people we work with.
I'm working directly with the behaviorist on staff to make sure I'm asking the right questions before I build anything. That part matters more than the tools. Collecting data without a well-formed hypothesis, without understanding what you're actually trying to learn, is just noise with extra steps.
The infrastructure is being built. The questions are being refined. The project is early but it's real.
I considered starting with publicly available datasets — and maybe I will later, for something separate. But right now I have something more interesting: the ability to collect my own data, build my own hypothesis from the ground up, and potentially produce something that lands in a real environment with real stakes.