What Ten Thousand Homes Taught Me About Asking Better Questions by Muhammad Ali

What Ten Thousand Homes Taught Me About Asking Better Questions by Muhammad Ali - October 2026 Scholarship Essay

The academic project that changed how I think was my housing market analysis — a data dashboard of California housing built on more than 10,000 property records. The research question was simple: what actually drives the differences in housing prices across California counties? I'd taken enough statistics to know the textbook answer — location, size, income levels — but I wanted to see it myself, in real data, with my own code.

What sparked my curiosity wasn't the answer but the messiness of getting there. The data was deterministic synthetic data, which meant I had to think hard about what the numbers could and couldn't tell me. I cleaned, explored, and visualized, and somewhere in the middle of plotting price distributions against county-level features, I realized something my classes had never quite made visceral: data doesn't speak for itself. Every chart was a choice — what to include, what to normalize, what to leave out — and each choice changed the story. A model is an argument, not a mirror.

That deepened my understanding of data science more than any lecture. I'd come into the field thinking it was about finding the right answer hidden in the numbers. I left understanding it's about asking sharper questions and being honest about uncertainty. When a county's prices didn't fit my expectations, my first instinct was to blame the data; my second, better instinct was to interrogate my assumptions. That habit — curiosity first, humility always — is now the core of how I approach every project, including CareerAC, the AI planning tool I built for transfer students. The housing project taught me that the most important skill in data science isn't coding. It's knowing what you don't know, and being excited to find out.