5 ms·

This has so much more to do with the lack of easily monetizable applications of complex mathematics. I'm sure a significant number of engineers and STEM profess

by sp527 10y ago

This has so much more to do with the lack of easily monetizable applications of complex mathematics. I'm sure a significant number of engineers and STEM professionals feel (as I do) that they're deliberately eschewing those subjects not for a lack of interest, but rather as a response to market demand. The market of people who are genuinely passionate about complex subjects in math and science is saturated relative to available opportunities. It makes more sense for an intelligent person to take the lower overhead and more achievable approach to becoming a value creator (e.g. full stack engineer with a strong focus on product development) than waste time competing against the countless PhDs vacating academia. I use a similar argument for avoiding the ML/Deep Learning hype train. At a large corp, that job should be left to people who've spent a lot of time mastering the subject. And if you're using ML heavily in an early stage company and don't have a PhD, you may very well be out of your depth competitively or wasting your time optimizing prematurely. But even ignoring all of that: anyone who's either spent time on or interacted with a data science team understands how difficult it is to create value with ML as well as how intangible the value that's created can often be. I worked at a fairly well known company that told clients we have a data science team and could use ML, knowing full well that the team rarely if at all manages to generate meaningful insights, because dropping buzzwords is an essential branding tool. Here's a better approach and the crux of why higher math is often superfluous: the best way to create value is to specialize in problem-solving first principles and remain amenable to either adopting new skills ad hoc or hiring to fill any skill deficiencies. The caveat: if you're passionate about STEM and that's a higher priority than 'creating value' in a deterministic and practical way (and maybe it is and that's perfectly fine and even reasonable), then by all means indulge in it. But it's important to align your expectations about what you want to do with yourself with the way in which you spend your time. A lot of pain arises in misconceptions around the question of what we want and the reality of what we're doing.

mseebach 10y ago
> I'm sure a significant number of engineers and STEM professionals feel (as I do) that they're deliberately eschewing those subjects not for a lack of interest, but rather as a response to market demand That's the point -- the "math myth" is specifically in relation to math (and science) skills as being of particular importance as a comparative advantage over other countries (Russia, Germany, Japan, India, China) where these fields are perceived to be prioritised higher in education. If there's low market demand for the skills, it's unlikely that countries with a higher supply of those skills can leverage them to surge ahead in comparative advantages. That said, I don't think I agree with the conclusion. Most people who are good at something don't particularly know they are, and few can explain why they are, so just asking them is useless. Sure, math and science are probably of limited value in their distilled, pure forms, but my impression is that most successful people in computers and software do subconsciously draw on a fairly solid math/science basis on a daily basis, even if they never consciously sit down to apply science to a problem. I took particular issue with the Accenture anecdote - we can all laugh at useless consultants all day long, but I've met more than a few who can pick inconsistencies (not validate hard math, but "why is that number so low when that is so high, and is revenue in X really only a third of Y" style things) out of a wall of numbers in what seems like an instant. You don't do that without a solid math foundation, even if you don't think you use it.
- visarga 10y ago
  > not validate hard math, but "why is that number so low when that is so high, and is revenue in X really only a third of Y" style things Isn't that math pre-level 8? It's just simple algebra or arithmetic. Interesting how little use trigonometry and calculus have.
  - douche 10y ago
    I've always thought trigonometry was one of the more useful things I did learn in school. Comes in handy a lot with carpentry and building projects. It's kind of cool when you realize that most of the shortcuts and rules-of-thumb that the old timers and carpenters use all the time can be derived from trigonometry, even if they never formally learned it that way.
JackMorgan 10y ago
What would you study instead to remain valuable in the next two decades? I've gotten pretty deep into engineering: worked on games, mobile, web, FP, legacy code revival, a half dozen popular languages, automated testing, and people management. I'm looking for something new to study to add flexibility and "luck surface area" to my career. I was thinking ML would be another area of valuable study. Do you have any alterative suggestions?
- sp527 10y ago
  I think you're already doing the right thing in continually learning. No one can tell you whether or not studying ML will prove to be valuable with any certainty, so it might be a good bet to pursue it if it interests you and you have the spare time and especially if you're desperate to pivot your career towards it (then you're left with no options). I can't offer a definitive decisionmaking framework, except to say that if you want to monetize something, make sure you can (fairly obvious, but not always what happens with people in our industry).
- petra 10y ago
  Not him, but: It's clear that the trend in software is towards higher abstraction, and that's what makes selecting what to learn so hard. But maybe a heuristic: learn thing that could not be abstracted(unless AI appears), or at least choose a few abstractions up ahead. One such thing is desinging and developing domain-specific-languages, which also looks to be an important tool in some systems. Another thing is prototyping with very high-level tools, because it gains you experience in working with customers, extracting requirements, product management, etc.
yodsanklai 10y ago
> It makes more sense for an intelligent person to take the lower overhead and more achievable approach to becoming a value creator (e.g. full stack engineer with a strong focus on product development) I would say that the surest way to make money for a mathematicaly-inclined person is to graduate in maths from a prestigious school and work in finance. At least, that's how I feel when I look at alumni from my school. People basically could specialize in finance or CS. Those that went into finance make consistently much more than the others. I wish I knew that at the time. I thought banks were boring and unappealing places. But now I think finance is one of the rare field (if not the only) where you can earn a lot with a technical, non-managerial position.
- Davidp00 10y ago
  >Those that went into finance make consistently much more than the others Hm, I would never have guessed that. Does anyone have any data on this? The top 1% sure, but the average and median also?
  - wrong_variable 10y ago
    Finance is fairly broad. People in middle management whose job is basically to deal with paper work many times say they are "Working in Finance". Just like people working at helpdesk say they are working in "IT". Higher finance is filled with crazy maths, if you are interested in statistics and probability then finance is the best way to make a lot of money. Its also not "gambling,etc" - its educated decision making. People in higher management need to make a lot of very important decisions all the time. 90% of the time they just use their "gut feeling". This is where people with strong technical knowledge in finance comes in. Where to allocate money is a very hard question to answer, you can throw a random dart at your options or bring in complex ML models. The sky is the limit.
  - sotojuan 10y ago
    I have no data either but one point is that the "ceiling" is higher. Meaning, a non-management programmer has a rough salary ceiling. Few programmers are paid $400k and it's not a very realistic goal. My mathematics -> finance friends (that are very good) virtually have little to no ceiling and have already doubled my salary. Like @wrong_variable said though, it depends what jobs and if they make it through (it's pretty competitive), so you're getting survivor bias from me.
elliott34 10y ago
I wish everyone on r/MachineLearning and those preparing for data science careers would read this comment and heed the advice of those of us who have actually spent time on data science teams and have experienced all of this first hand.
nabla9 10y ago
The market for STEM professionals is superstar economy similar to the market of comedians, athletes, musicians and artists. The payout in the profession follows Zipf's distribution. btw. 12% of US millionaires are educators. http://www.barrons.com/articles/SB50001424053111904370004577390061053133138 http://www.barrons.com/articles/SB50001424053111904370004577...? The Economics of Superstars Sherwin Rosen The American Economic Review Vol. 71, No. 5 (Dec., 1981), pp. 845-858 https://www.jstor.org/stable/1803469 https://www.jstor.org/stable/1803469?
sidlls 10y ago
I want everyone to read and re-read your bit about data science and machine learning. Many times. I think even people in the software industry underestimate both how accurate and how difficult it is to employ statistics to produce something truly meaningful. My current job is on a data science team. I find it amusing that the business folks are able to sell our product, and then sigh to myself and do a little crying inside when I realize how it's possible.
- quantumhobbit 10y ago
  To most people machine learning and data science are magic. They either believe in magic or they don't. Once you learn it with sufficient mathematical sophistication, it stops being magic and starts being a tool that works in some situations and not in others. You are surrounded by people who believe in the magic and will buy anything whether it works or not. Equally frustrating is being surrounded by non-believers who don't accept that simple things are actually possible. We are still on the upswing for now so there are more believers than not. But if another AI winter happens, be prepared for the mbas to reject applications of data science that make complete sense because "we tried that data science stuff and it doesn't work".
  - eli_gottlieb 10y ago
    Well, as someone who took a solid class in machine-learning, has an understanding of why it works, and regards it far more as science than magic, "we" who know it damn well committed a sin and sold our souls for funding if "we" just told everyone else it was magic. Now, admittedly, I think the deep-learning folks are so glad to finally have their faith in neural networks vindicated that they've let themselves buy into their own propaganda, but that doesn't mean there's any actual magic!
quantumhobbit 10y ago
You know I might actually take a pay cut for a more mathematical role over my current situation 'creating value'. I would love for my work to be driven more by logic and data than big personalities and office politics.

by sp527 10y ago

mseebach 10y ago

> I'm sure a significant number of engineers and STEM professionals feel (as I do) that they're deliberately eschewing those subjects not for a lack of interest, but rather as a response to market demand That's the point -- the "math myth" is specifically in relation to math (and science) skills as being of particular importance as a comparative advantage over other countries (Russia, Germany, Japan, India, China) where these fields are perceived to be prioritised higher in education. If there's low market demand for the skills, it's unlikely that countries with a higher supply of those skills can leverage them to surge ahead in comparative advantages. That said, I don't think I agree with the conclusion. Most people who are good at something don't particularly know they are, and few can explain why they are, so just asking them is useless. Sure, math and science are probably of limited value in their distilled, pure forms, but my impression is that most successful people in computers and software do subconsciously draw on a fairly solid math/science basis on a daily basis, even if they never consciously sit down to apply science to a problem. I took particular issue with the Accenture anecdote - we can all laugh at useless consultants all day long, but I've met more than a few who can pick inconsistencies (not validate hard math, but "why is that number so low when that is so high, and is revenue in X really only a third of Y" style things) out of a wall of numbers in what seems like an instant. You don't do that without a solid math foundation, even if you don't think you use it.

visarga 10y ago
> not validate hard math, but "why is that number so low when that is so high, and is revenue in X really only a third of Y" style things Isn't that math pre-level 8? It's just simple algebra or arithmetic. Interesting how little use trigonometry and calculus have.
- douche 10y ago
  I've always thought trigonometry was one of the more useful things I did learn in school. Comes in handy a lot with carpentry and building projects. It's kind of cool when you realize that most of the shortcuts and rules-of-thumb that the old timers and carpenters use all the time can be derived from trigonometry, even if they never formally learned it that way.

JackMorgan 10y ago

What would you study instead to remain valuable in the next two decades? I've gotten pretty deep into engineering: worked on games, mobile, web, FP, legacy code revival, a half dozen popular languages, automated testing, and people management. I'm looking for something new to study to add flexibility and "luck surface area" to my career. I was thinking ML would be another area of valuable study. Do you have any alterative suggestions?

sp527 10y ago
I think you're already doing the right thing in continually learning. No one can tell you whether or not studying ML will prove to be valuable with any certainty, so it might be a good bet to pursue it if it interests you and you have the spare time and especially if you're desperate to pivot your career towards it (then you're left with no options). I can't offer a definitive decisionmaking framework, except to say that if you want to monetize something, make sure you can (fairly obvious, but not always what happens with people in our industry).
petra 10y ago
Not him, but: It's clear that the trend in software is towards higher abstraction, and that's what makes selecting what to learn so hard. But maybe a heuristic: learn thing that could not be abstracted(unless AI appears), or at least choose a few abstractions up ahead. One such thing is desinging and developing domain-specific-languages, which also looks to be an important tool in some systems. Another thing is prototyping with very high-level tools, because it gains you experience in working with customers, extracting requirements, product management, etc.

yodsanklai 10y ago

> It makes more sense for an intelligent person to take the lower overhead and more achievable approach to becoming a value creator (e.g. full stack engineer with a strong focus on product development) I would say that the surest way to make money for a mathematicaly-inclined person is to graduate in maths from a prestigious school and work in finance. At least, that's how I feel when I look at alumni from my school. People basically could specialize in finance or CS. Those that went into finance make consistently much more than the others. I wish I knew that at the time. I thought banks were boring and unappealing places. But now I think finance is one of the rare field (if not the only) where you can earn a lot with a technical, non-managerial position.

Davidp00 10y ago
>Those that went into finance make consistently much more than the others Hm, I would never have guessed that. Does anyone have any data on this? The top 1% sure, but the average and median also?
- wrong_variable 10y ago
  Finance is fairly broad. People in middle management whose job is basically to deal with paper work many times say they are "Working in Finance". Just like people working at helpdesk say they are working in "IT". Higher finance is filled with crazy maths, if you are interested in statistics and probability then finance is the best way to make a lot of money. Its also not "gambling,etc" - its educated decision making. People in higher management need to make a lot of very important decisions all the time. 90% of the time they just use their "gut feeling". This is where people with strong technical knowledge in finance comes in. Where to allocate money is a very hard question to answer, you can throw a random dart at your options or bring in complex ML models. The sky is the limit.
- sotojuan 10y ago
  I have no data either but one point is that the "ceiling" is higher. Meaning, a non-management programmer has a rough salary ceiling. Few programmers are paid $400k and it's not a very realistic goal. My mathematics -> finance friends (that are very good) virtually have little to no ceiling and have already doubled my salary. Like @wrong_variable said though, it depends what jobs and if they make it through (it's pretty competitive), so you're getting survivor bias from me.

elliott34 10y ago

I wish everyone on r/MachineLearning and those preparing for data science careers would read this comment and heed the advice of those of us who have actually spent time on data science teams and have experienced all of this first hand.

nabla9 10y ago

The market for STEM professionals is superstar economy similar to the market of comedians, athletes, musicians and artists. The payout in the profession follows Zipf's distribution. btw. 12% of US millionaires are educators. http://www.barrons.com/articles/SB50001424053111904370004577390061053133138 http://www.barrons.com/articles/SB50001424053111904370004577...? The Economics of Superstars Sherwin Rosen The American Economic Review Vol. 71, No. 5 (Dec., 1981), pp. 845-858 https://www.jstor.org/stable/1803469 https://www.jstor.org/stable/1803469?

sidlls 10y ago

I want everyone to read and re-read your bit about data science and machine learning. Many times. I think even people in the software industry underestimate both how accurate and how difficult it is to employ statistics to produce something truly meaningful. My current job is on a data science team. I find it amusing that the business folks are able to sell our product, and then sigh to myself and do a little crying inside when I realize how it's possible.

quantumhobbit 10y ago
To most people machine learning and data science are magic. They either believe in magic or they don't. Once you learn it with sufficient mathematical sophistication, it stops being magic and starts being a tool that works in some situations and not in others. You are surrounded by people who believe in the magic and will buy anything whether it works or not. Equally frustrating is being surrounded by non-believers who don't accept that simple things are actually possible. We are still on the upswing for now so there are more believers than not. But if another AI winter happens, be prepared for the mbas to reject applications of data science that make complete sense because "we tried that data science stuff and it doesn't work".
- eli_gottlieb 10y ago
  Well, as someone who took a solid class in machine-learning, has an understanding of why it works, and regards it far more as science than magic, "we" who know it damn well committed a sin and sold our souls for funding if "we" just told everyone else it was magic. Now, admittedly, I think the deep-learning folks are so glad to finally have their faith in neural networks vindicated that they've let themselves buy into their own propaganda, but that doesn't mean there's any actual magic!

quantumhobbit 10y ago

You know I might actually take a pay cut for a more mathematical role over my current situation 'creating value'. I would love for my work to be driven more by logic and data than big personalities and office politics.