Big O is okay but for some reason when professors tell you that reasoning about universally valid performance characteristics of an algorithm is a lot of work if you are worrying about every tiny implementation detail the students ignore all of it and always disregard the constants even though the professor explicitly says that this is a method for estimating how fast the execution time of a function grows with increasing input. It's about building algorithms or datastructures and comparing them. Once you have a good baseline you can improve it further through engineering. The irony of course is that intuition and experience alone can get you 90% of the way but at a much lower time investment but theory can never replace experience because you have to turn that theory into experience first.