At NYU KNESIS, our dance club hosts semesterly large showcases with food and drinks for attendees. After one event, I analyzed our leftover inventory and noticed a clear pattern: certain food items consistently went untouched, while drinks sold out quickly.

I decided to track preferences by surveying attendees and comparing leftover food and attendance data. Based on those insights, I reallocated part of our event budget away from low-demand food items and toward popular boba drinks from a local shop near NYU.

At our next showcase, this small data-driven change resulted in a 15% increase in attendance and higher overall satisfaction ratings in post event feedback. It reinforced to me how data analysis, even on a smaller organizational scale, can lead to tangible improvements in engagement and resource efficiency.

I prioritize data quality and accuracy by combining structured verification processes with a strong attention to detail. For example, when managing analytics for Bite Club events, I consistently cross checked vendor sales data and attendance metrics across multiple sources before reporting trends to our marketing team.

In general, I approach data work by cleaning the data, then analyzing it. Making sure data is complete, consistent, and contextually accurate before concluding. I also document every step of the process, which helps maintain transparency and reproducibility for both my teammates and stakeholders.

Yes, I am a sophomore.