EE is one of those "engineering is really applied physics" disciplines. There's a slant towards standardised EE-specific solutions for PDEs, but it's still much more abstract and mathematical than any field of CS, apart from maybe cryptography and data science.
But career-wise, it's a mediocre choice in most Western countries. (Possible exception is Germany, where engineers have a similar status to doctors and lawyers.)
Most people have no clue what EE even is, or just how much math and engineering goes into building everyday devices and services.
(A friend of the family said "Great! You'll be able to get a job repairing TVs!" when I got my course offer.)
> There's a slant towards standardised EE-specific solutions for PDEs, but it's still much more abstract and mathematical than any field of CS, apart from maybe cryptography and data science.
Here's the thing, though: PDEs are NP-hard. There isn't a generalizable way to model dynamics. On the other hand, dynamics come up everywhere:
- How is the pandemic going to evolve?
- How will incentive structures skew cultures?
- How do I build a suspension for my car?
- How does heat leak from my house?
- How does my understanding evolve with learning?
... and so on.
What EE does -- and I think uniquely -- is given intuitive, graphical tools to think about differential equations, in tools like Laplace, Body, Nyquist, root-locus, and so on.
They also give a lot of applied experience in applying those, including in contexts with nonlinearities. An op amp will clip on both sides, which you model as a linear differential equation (which is easy enough to reason about) and a memoryless, time-invariant nonlinearity. You squint. You kinda ask yourself how it would work if it /were/ linear, and the nonlinearity just cut gain. And at some point, after doing it enough, you have intuition for what it will do.
With the EE-specific stuff, I can intuitively reason about these things think through to design.
EE is all about modeling -- building simpler equations which approximate more complex ones in ways which give intuition -- so this is also usually correct or almost correct. Indeed, if you go onto grad level courses in control theory, you'll see formalizations of this intuition, where for example, a time-variant system or a nonlinear system is modeled as a linear time-invariant system, together with a bounded error.
A lot of the mathy stuff -- which I've learned a fair bit of as well -- is in abstract more general, but in practice, gives much less intuition.
My experience with the real world is that there are rarely actual differential equations handed to me. I kinda get that we've set up some pricing structure, or some incentive design, or whatnot, but I can't model it formally. I know which way things push, and whether those integrate or not. I can draw a block diagram and reason about how it will behave, in a way the math side doesn't let me do.
>Germany, where engineers have a similar status to doctors and lawyers
Errr, no they don't. In terms of pay and status Doctors and Lawyers trump Engineers every day of the week in Germany, the only exception being the engineers with PhDs who are tech leads in some well known research institute or big-brand company like Audi or Porsche.
But career-wise, it's a mediocre choice in most Western countries. (Possible exception is Germany, where engineers have a similar status to doctors and lawyers.)
Most people have no clue what EE even is, or just how much math and engineering goes into building everyday devices and services.
(A friend of the family said "Great! You'll be able to get a job repairing TVs!" when I got my course offer.)