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An EE degree IS awesome. It gives a generalizable mathematical foundation which applies in just about any other field of work. It gives a huge competitive edge.

An EE degree IS awesome. It gives a generalizable mathematical foundation which applies in just about any other field of work. It gives a huge competitive edge. For example: * Circuits are physical implementations of differential equations, and EE gives a unique way to intuitively think about dynamics, which applies to finance, epidemiology, and just a really diverse range of domains. * With a rigorous EE background, you can rapidly pick up most domains of engineering (think Elon Musk), since you've got all the mathematical foundations. The reverse isn't true. The way math is taught in EE is broader than e.g. mechanical, civil, or other engineering disciplines, where it tends to be more domain-specific. EE gives you a lot of depth in probability, error analysis, signal processing, controls, calculus, linear algebra, etc. I think the only things missing are statistics and discrete math, and I picked those up elsewhere. High-performance board-level EE is insanely fun. Incredibly creative. You get to build stuff, design stuff, do math, and it's just a huge diversity of intellectually-fulfilling stuff. IC design is a bit less fun, due to the many-month turn cycles (develop, wait months and hundreds of thousands dollars, and test/debug), but not bad. However, the EE industry sucks: - Pay is not bad, but much worse than other jobs you can get with an EE degree. - Work culture has all the worst excesses of the eighties -- think of Office Space style cubicle farms, dress codes, conservative management, ISO processes, and paperwork. - Yet it's somehow adopted some of the worst excesses of the 2010s; it no longer feels like work is a family or a community - And it's hard to get into. There are virtually no jobs for junior-level EEs (which isn't just BSes -- in the Great Recession, I knew bright newly-minted Stanford/MIT/Caltech/etc. Ph.Ds who couldn't find jobs). - Even at the senior-level, there's a bit too much of a boom-and-bust cycle, without the booms ever getting that boomy, but the busts being pretty busty. I spent maybe five years doing EE work after my EE degree, and I think that was enough. I've been out for a long time now. I still do EE as a hobby, and I enjoy it, but the industry culture isn't one I remember with fondness. I suspect a lot of this stuff will continue to disappear from the US into Asia; that transition is rapidly in progress. US firms maintain specialized knowledge in some areas (e.g. particular types of MEMS devices), but there are plenty of places we've fallen behind. I don't see us on the path to regain leadership. I think some of this is cyclical. Declining industries don't make for good employers, and poor employers don't make for growth industries.

TheOtherHobbes 5y ago
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.)
- ChuckNorris89 5y ago
  >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.
- murgindrag 5y ago
  > 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.

TheOtherHobbes 5y ago

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.)

ChuckNorris89 5y ago
>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.
murgindrag 5y ago
> 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.