I came across a reference to the Douglas-Peucker line simplification algorithm in the context of lossy compression for GPS tracks. It's a beautiful thing, but not widely known, perhaps? It made me think: what other gems are there that are not part of the standard CS 101 curriculum? Kalman filtering?
https://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm
The Marchenko-Pastur distribution derives from random matrix theory a nice theoretical border to estimate if a principal component is more noise then data.
Also I am a huge fan of all sorts of embedding/projection/matrix factorization algorithms and I use them quite regularly.