University Ranking Backend: Code Deep Dive (Part 2)
In Part 1, we explored the core search logic and API structure. In this second part, we’ll dig into the Data Layer—how data is normalized, stored, and retrieved in detail. We’ll also look at the utility scripts that power the backend’s intelligence. 1. The Data “Glue”: utils/normalize_name.py One of the biggest challenges in aggregating data from multiple sources (QS, US News, Niche) is that they all name universities differently. “MIT” might be “Massachusetts Institute of Technology” in one dataset and “Mass Inst of Tech” in another. ...