Throughout this course, the main problem I was solving was how to efficiently store, organize, and retrieve course data for an advising program. The goal wasn’t just to “make it work,” but to compare different data structures and understand how their performance impacts real-world use. Designing the Binary Search Tree and related logic really forced me to think about how data flows through a system, especially when it comes to searching, sorting, and validating prerequisites. Understanding data structures turned out to be the point of the whole project, because the way you store data directly affects how fast and cleanly you can access it later.
I approached the problem step by step: first focusing on parsing and validating the file correctly, then implementing each data structure, and finally analyzing performance. "Roadblocks" definitely came up, especially around making sure prerequisites were validated properly and comparing runtime behavior in a meaningful way, but breaking the program into smaller functions made debugging manageable (as it always does). Over time, my mindset shifted from just writing code that compiles to writing code that is modular, readable, and adaptable (the basic rule in Computer Science). I paid more attention to separating concerns, consistent naming, and clean function design. This project expanded how I think about software design overall; I now consider performance, scalability, and maintainability from the beginning instead of as an afterthought. It’s changed how I write programs that are less rushed, more intentional, and built in a way that I (or anyone else) could realistically maintain and extend later. It all comes down to code early, test often, write comments (I'm bad at this last part).