I had a similar frame of mind when I was a student. I didn't pay too much attention to the formal details; I didn't brush it off, to be fair, and I think I paid more attention to it than most of my colleagues, but I still massively underestimated how much I really needed it.
Reality kindda bitchslapped me later. Turns out the theoretical understanding of even the most basic and otherwise fundamental aspects of my slice of engineering (EE) is pretty much essential whenever you need to design something that is non-trivial and not previously implemented by someone else and available on the Internet. All those circuits I tweaked and combined were great study material, but when it came to doing something on my own that I couldn't quite find elsewhere, which had to respond to actual hard requirements (no more than this many mV of ripple on this signal and THD below this margin), it wasn't an enjoyable ride. And then there are whole branches of EE where you don't really get to prototype stuff on a breadboard; at one point I thought I wanted to get into microelectronics (programming got the best of me though). There are literally thick books about how transistors behave, and you literally need to understand that stuff if you want to make anything slightly more reliable than a 741.
To this day, I still value intuitive understanding more than the bland math. It's the idea percolator that makes you come up with clever designs and lean, elegant solutions, but it's the bland math that actually enables you to turn it into something that can be reliably manufactured and exploited.
Analog electronics is a unique, not representative case.I think it depends greatly on the field and application. Some applications are too complex for math, so you just have to create really good simulations, like for example in wireless communications.
In other applications , value comes through integration, managing complexity ,smart component selection, and rapid development and combining multiple knowledge domains.One such example is smart watches. Some of the theory is less valuable there.
Some applications are too complex for math, so you just have to create really good simulations, like for example in wireless communications.
Understanding the math behind those simulations is absolutely crucial to not getting bit by them.
One such example is smart watches. Some of the theory is less valuable there.
The experience of working on low-power devices with engineers that do not understand the theoretical fundamentals of the devices they work with is an experience I can only describe as very, very painful. Smart watches are an excellent illustration of this, yes. "Smart" component selection with no understanding of the underlying complexity of the technology is a sure way to ensure you end up with crap that's 30% more expensive and 50% more power hungry than it should be.
"Smart" component selection with no understanding of the underlying complexity of the technology is a sure way to ensure you end up with crap that's 30% more expensive and 50% more power hungry than it should be.
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I had a similar frame of mind when I was a student. I didn't pay too much attention to the formal details; I didn't brush it off, to be fair, and I think I paid more attention to it than most of my colleagues, but I still massively underestimated how much I really needed it.
Reality kindda bitchslapped me later. Turns out the theoretical understanding of even the most basic and otherwise fundamental aspects of my slice of engineering (EE) is pretty much essential whenever you need to design something that is non-trivial and not previously implemented by someone else and available on the Internet. All those circuits I tweaked and combined were great study material, but when it came to doing something on my own that I couldn't quite find elsewhere, which had to respond to actual hard requirements (no more than this many mV of ripple on this signal and THD below this margin), it wasn't an enjoyable ride. And then there are whole branches of EE where you don't really get to prototype stuff on a breadboard; at one point I thought I wanted to get into microelectronics (programming got the best of me though). There are literally thick books about how transistors behave, and you literally need to understand that stuff if you want to make anything slightly more reliable than a 741.
To this day, I still value intuitive understanding more than the bland math. It's the idea percolator that makes you come up with clever designs and lean, elegant solutions, but it's the bland math that actually enables you to turn it into something that can be reliably manufactured and exploited.
Analog electronics is a unique, not representative case.I think it depends greatly on the field and application. Some applications are too complex for math, so you just have to create really good simulations, like for example in wireless communications.
In other applications , value comes through integration, managing complexity ,smart component selection, and rapid development and combining multiple knowledge domains.One such example is smart watches. Some of the theory is less valuable there.
Understanding the math behind those simulations is absolutely crucial to not getting bit by them.
The experience of working on low-power devices with engineers that do not understand the theoretical fundamentals of the devices they work with is an experience I can only describe as very, very painful. Smart watches are an excellent illustration of this, yes. "Smart" component selection with no understanding of the underlying complexity of the technology is a sure way to ensure you end up with crap that's 30% more expensive and 50% more power hungry than it should be.
Can you please expand on that ?