I think an important lesson for any grad student is to learn to read through the bullshit in papers and try and understand what the authors actually did.
It helps a lot that in CS you can often see the code that the authors published along with the paper. Just staring at formulae doesn't mean much, because for all you know the author just hammed up the equations to get their paper into a top conference. That's not to say that the equations are excessive, or the authors are being misleading, but I think there is definitely an expectation in some fields that putting equations in makes your paper look clever even if they're broadly unecessary.
It's also wildly different depending on the field. If you look at variational methods in computer vision, images are [continuous] mappings from some domain onto the reals (I : Ω->R3 for colour). Does that change the fact that an image in memory is just a bunch of numbers in a grid? Not really, but it's bloody confusing the first time you see it.
This doesn't help with understanding the maths, but at some point you have to give up and say "This guy proved it, and someone else peer reviewed it, so I can use it to solve my problem". It's perfectly OK to stand on other people's work and still make creative contributions to your field, that's the point of research.