For interviewing at graduate school, one has to be accepted first, which requires taking the GREs and having a undergraduate degree. I was applying for biomedical engineering, which is what my undergraduate degree was in. For this University, you are accepted or not accepted before they even meet you. It is just based on grades, GREs, essays, letters of recommendations. If you are accepted, they fly you out there for a weekend visit with all the other prospective graduate students to check out the campus and town. You get to meet with professors of your choice to talk about research, and there are plenty of social opportunities with the students and with the professors.

During the application season, I made a conscious decision not to fixate solely on standardized test scores—though I certainly didn't let my guard down. Instead, I focused my energy on a critical question: 'As someone aspiring to build a career in Data Science, where are the gaps in my current skill tree?' With this in mind, I reviewed my past experiences, ranging from academic research to previous internships. Rather than simply listing tasks, I focused on articulating the problem-solving logic behind my work and demonstrating exactly how I leveraged data to drive decision-making.