To me the whole point of a university course is to learn part of the theoretical knowledge you would not learn spontaneously by practicing. That includes way more than just algorithms and datastructures.

For example, a good knowledge of language theory will prevent engineers from creating scripting languages that cannot be parsed. Probabilities are everywhere in machine learning. Many engineers work with by-products of operational research and need to understand the theory behind them to make them efficient. Complex numbers and trigonometry (quaternions etc.) are needed to build even basic 3D engines. Recent probabilistic data structures such as hyperloglog are being integrated into modern database systems. Good understanding of operating systems is useful for security and parallel programming...

It depends on your area. I am a software engineer and write software for biomedical signal analysis and finance/trading, and I promise you it is very math intensive. There is a pretty wide gap between a SE creating websites and a SE designing trading systems or medical devices.

The math involved in these can be reduced to replace|reduce operations|calculation|procedures in existing algorithms for the most part (or not)? If you need to devise new algorithms for a stock trade or for pattern recognition, signal inhibition or enhancement, etc.. based on domain you might be in an area I don't recognize.

As others have pointed out, some math is CS that is almost independent of the problem domain, and then there is obviously lots of math that is domain dependent. I use tons of linear algebra, trig etc. in my field (CAD).

Well done! These are the two topic areas that are really interesting for the practically minded as well. It does help to like math rather than hate it for a software engineer but refined math is mostly out of scope for most programmers.