> On the rare occasion that I need to use a data structure or an algorithm to optimize for Time Space complexity, I'll either use a community built package or a language level feature. I'll never invent my own because whatever I come up with will be half baked anyway.

In these cases you recognized you needed to use a specific algorithm or data structure and was aware of it's existence, name and properties. That's most of the work.

Contrast this with code where common data structures are re-implemented (I need a dictionary and all I know is an array, better iterate and compare the keys until I find the right thing). And that's only scratching the surface of bad code, believe me.

Those who made “mistakes” like re-implementing basic data structure or algorithm will learn much more than what compsci degree can teach them.

It's not a mistake to re-implement these. It's a mistake not to recognize that you need them in the first place.

Nobody wants this type of experimentation on a production code base though.

>> if I'm being 100% honest, I don't even recall the majority of them.

100% this. A lot of these concepts go over your head if you haven't seen a practical application for them. By the time you've encountered such a use in industry, the knowledge is long forgotten.

Yes, but the point of memorisation is you can much quicker re-learn the subject and you know there is a solution to a problem when you face it. If you have never learnt it, you don't know that there is a solution, or you start to optimise things in a way that is proven to be wrong. You might not able to recall them, but the knowledge is likely there in your subconscious.