4 ms·

statistics is pretty useful in computer science...and as someone has has worked in biotech on the software side...chemistry can be as well. Heck even anthropolo

statistics is pretty useful in computer science...and as someone has has worked in biotech on the software side...chemistry can be as well. Heck even anthropology and philosophy were pretty relevant when I took my courses on NLP years ago.

tartuffe78 4y ago
As someone with 10 years as a software engineer I haven't needed it.
- gunfighthacksaw 4y ago
  I’m sure there are 10y SWEs (and 1 yr) that use it every day. Obviously it’s going to be irrelevant to compilers or something, but not to heuristic approaches or anything with some quantifiable (and actively quantified) nondeterminism.
  - kolbusa 4y ago
    It will be very relevant in any field that does performance testing. And that includes compilers.
    - edflsafoiewq 4y ago
      I don't think you need a statistics class to do performance testing.
      - tetromino_ 4y ago
        You change an optimizer pass. Looks good on a microbenchmark. But then you try it out on some samples of real code. Turns out, it probably makes some real code a little bit faster. No difference on other real code, but the data are noisy, so it's hard to tell. And on one piece of code, it seems to actually cause a regression - but again, the data from multiple runs are noisy. Should you enable the optimizer change by default, or not? Or do you still need to collect more data? How much more data? What data - more runs or more different code samples? How confident do you want to be, and how confident can you be? These are questions you will face in your real day to day work, and a few statistics courses will be incredibly helpful to you in answering them.
        edflsafoiewq 4y ago
        I knew someone would say something like that, but I've never seen that sort of thing done personally and I really doubt lack of statistical knowledge is even close to the biggest obstacle to writing faster software. For micro-optimizations so subtle you need fancy techniques to even tell they work, non-quantitative factors (code impact, will it enable other optimizations, etc.) are more likely to be decisive. Techniques to reduce noise are either non-statistical (warming up caches) or unsophisticated (average many trials, best of three).
derangedHorse 4y ago
The amount of programming jobs (out of all possible programming positions available) that require proper statistics or chemistry for that matter are so low that it doesn't make sense to learn those subjects (in the context of computer science) preemptively. Even if people who take and succeed in subjects like Chemistry who do end up in a job that may use it down the line probably won't remember what they learned anyway.
- WalterBright 4y ago
  The jobs don't require it, but the people who are not aware of statistics or chemistry are unable to recognize when it would help them. I.e. it becomes a self-fulfilling prophecy. (It has nothing to do with "in the context of computer science").
  - derangedHorse 4y ago
    I imagine they'd either have coworkers who are more involved in those domains (if actually needed for the job) who can guide them on the right path or can just google their problems and end up on a path of self-discovery that leads them to those subjects. Prematurely surveying other subjects not pertinent to the job one is going to university in the first place for seems silly in that regard. I'm sure many would rather focus their time on learning more of the things that are shared in the majority of the job market (or at least more things that actually interest them).
    - WalterBright 4y ago
      You'd imagine wrong. (In my experience.) For example, in mechanical engineering, I know how to make reliable systems out of unreliable parts. Software engineering is still focused on a hopeless quest to make software perfect. How to do it assuming unreliability has been slowly seeping into software engineering the hard way, bit by bit, over my entire career. One example: running the brake software on the same computer that is connected wirelessly to the internet.
      - 4y ago
justbaker 4y ago
I’d say psychology is a good a thing to know as well.

tartuffe78 4y ago

As someone with 10 years as a software engineer I haven't needed it.

gunfighthacksaw 4y ago
I’m sure there are 10y SWEs (and 1 yr) that use it every day. Obviously it’s going to be irrelevant to compilers or something, but not to heuristic approaches or anything with some quantifiable (and actively quantified) nondeterminism.
- kolbusa 4y ago
  It will be very relevant in any field that does performance testing. And that includes compilers.
  - edflsafoiewq 4y ago
    I don't think you need a statistics class to do performance testing.
    - tetromino_ 4y ago
      You change an optimizer pass. Looks good on a microbenchmark. But then you try it out on some samples of real code. Turns out, it probably makes some real code a little bit faster. No difference on other real code, but the data are noisy, so it's hard to tell. And on one piece of code, it seems to actually cause a regression - but again, the data from multiple runs are noisy. Should you enable the optimizer change by default, or not? Or do you still need to collect more data? How much more data? What data - more runs or more different code samples? How confident do you want to be, and how confident can you be? These are questions you will face in your real day to day work, and a few statistics courses will be incredibly helpful to you in answering them.
      - edflsafoiewq 4y ago
        I knew someone would say something like that, but I've never seen that sort of thing done personally and I really doubt lack of statistical knowledge is even close to the biggest obstacle to writing faster software. For micro-optimizations so subtle you need fancy techniques to even tell they work, non-quantitative factors (code impact, will it enable other optimizations, etc.) are more likely to be decisive. Techniques to reduce noise are either non-statistical (warming up caches) or unsophisticated (average many trials, best of three).

derangedHorse 4y ago

The amount of programming jobs (out of all possible programming positions available) that require proper statistics or chemistry for that matter are so low that it doesn't make sense to learn those subjects (in the context of computer science) preemptively. Even if people who take and succeed in subjects like Chemistry who do end up in a job that may use it down the line probably won't remember what they learned anyway.

WalterBright 4y ago
The jobs don't require it, but the people who are not aware of statistics or chemistry are unable to recognize when it would help them. I.e. it becomes a self-fulfilling prophecy. (It has nothing to do with "in the context of computer science").
- derangedHorse 4y ago
  I imagine they'd either have coworkers who are more involved in those domains (if actually needed for the job) who can guide them on the right path or can just google their problems and end up on a path of self-discovery that leads them to those subjects. Prematurely surveying other subjects not pertinent to the job one is going to university in the first place for seems silly in that regard. I'm sure many would rather focus their time on learning more of the things that are shared in the majority of the job market (or at least more things that actually interest them).
  - WalterBright 4y ago
    You'd imagine wrong. (In my experience.) For example, in mechanical engineering, I know how to make reliable systems out of unreliable parts. Software engineering is still focused on a hopeless quest to make software perfect. How to do it assuming unreliability has been slowly seeping into software engineering the hard way, bit by bit, over my entire career. One example: running the brake software on the same computer that is connected wirelessly to the internet.
    - 4y ago

justbaker 4y ago

I’d say psychology is a good a thing to know as well.