The majority of electronics/electrical engineering courses are spent teaching you advanced math and physics that most will never end up using. Unless you go back and teach it that is.
I thought the same for a long time. Until I had to fix a certain embedded system. Some value drifted off / just didn't add up after 72 hours of operation. And it was a system to run 24*7.
The previous four programmers "fixed" it by introducing correction factors. The first implemented the original software and "fixed" it using a single correction factor. The second had added a linear correction function, the third fine-tuned the correction factors, the forth added yet another correction function.
All these worked, for some value of "worked", with certain input parameters (the once they tested with), but totally blow up with others. So the company decided to hire an engineer instead of cowboy programmers to fix it.
To cut a long story short, after a week I found the core function in the system was an inverted statistic distribution function for mapping the input values to output values while enforcing a certain distribution of the output values. Everything looked OK. I calculated the rounding errors, they couldn't accumulate to the observed errors. I integrated the function over time and then it suddenly didn't look so great.
I digged out my old statistics textbooks and started reading. I was glad that I still remembered 10% of the basics and upset that I had forgotten 90%. With what I was still remembering from uni it turned out the distribution function that was used was a continuous one, inverted to produce the desired distribution. But the inverted function was only evaluated at fixed, discreet points and rounded to the nearest discrete output value. This didn't add up to a probability of one and accounted for the drift of the output.
More textbook studies and googling brought a discrete distribution function to light that was simple to implement, had the desired distribution properties and didn't suffer the errors of the original one.
That was good money and more jobs. And I was so glad I once had learned some math and statistics at uni, as opposite to the cowboy programmers.