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I agree that they are not programmers but in my opinion it cannot become an explanation to write a code which become non repeatable, especially if they have big

I agree that they are not programmers but in my opinion it cannot become an explanation to write a code which become non repeatable, especially if they have big impact on how the flow will look on production environment.

BiteCode_dev 5y ago
Scientists are not meant to create code that ends up on production. Once they have a working concept, they should team up with a programmer to make it live. Again, if it's not possible, then you accept the imperfection of the result, or provide better tooling. There is no blame to put on them whatsoever.
- throwaway210222 5y ago
  I disagree, many, many scientists hire a professional statistician to do the stats for their papers. Similarly, they should hire experienced, qualified software engineers to write/check the software in their papers. They don't because 'everyone can code - its just logic'.
  - BiteCode_dev 5y ago
    Err... That's kinda my point ?
    - throwaway210222 5y ago
      Mea culpa - I interpreted your comment to mean: only once its ready to go into industrial use.
- jstx1 5y ago
  > Scientists are not meant to create code that ends up on production. You might be mixing up the terms. I don't think that the point was about "scientists" in general, it was about "data scientists". The first is a common term used to describe someone who does science in some professional capacity. The second one is a very broad job title within software which very often includes writing code that ends up in production - at some data science roles that might even be your main responsiblity.
  - BiteCode_dev 5y ago
    Even among devs with very close speciality, the difference in productivity is immense. Take a iOS team, make them dev a MacOS desktop app, and see their output plummet in their of productivity or quality. They will end up doing a good job, but it will take between 6 months and a year to catch up with a specialized team. A data scientist is not even a somebody trained as a programmer. Their strong suit is data analysis, and it turns out one of the tool to manipulate data today are programming languages so their do it. But I as a Python trainer, I train data analyst regularly, and they don't have a clue about language ecosystems, how the OS work, data formats or reliable software architecture. They mainly want to output their graph, pdf report or other media to serve their conclusion. They may want to create some reusable algo, or machine learning model, but that's the limit most of them hit. If one take their code and put it in prod (which I know happens, don't get me wrong), that's not the data scientist fault. They are doing their job, in which programming is just one of the many means to an end, and is not their specialty.
    - jstx1 5y ago
      > But I as a Python trainer, I train data analyst regularly, and they don't have a clue about language ecosystems, how the OS work, data formats or reliable software architecture. This is like teaching some JavaScript to complete beginners at a bootcamp and then declaring that front-end developers aren't real programmers because they know so little.
      - BiteCode_dev 5y ago
        Oh no, I train frontend developers, and they know a lot more than data scientists when it's about programming. I'm not talking about complete beginners. I don't train beginners.

BiteCode_dev 5y ago

Scientists are not meant to create code that ends up on production. Once they have a working concept, they should team up with a programmer to make it live. Again, if it's not possible, then you accept the imperfection of the result, or provide better tooling. There is no blame to put on them whatsoever.

throwaway210222 5y ago
I disagree, many, many scientists hire a professional statistician to do the stats for their papers. Similarly, they should hire experienced, qualified software engineers to write/check the software in their papers. They don't because 'everyone can code - its just logic'.
- BiteCode_dev 5y ago
  Err... That's kinda my point ?
  - throwaway210222 5y ago
    Mea culpa - I interpreted your comment to mean: only once its ready to go into industrial use.
jstx1 5y ago
> Scientists are not meant to create code that ends up on production. You might be mixing up the terms. I don't think that the point was about "scientists" in general, it was about "data scientists". The first is a common term used to describe someone who does science in some professional capacity. The second one is a very broad job title within software which very often includes writing code that ends up in production - at some data science roles that might even be your main responsiblity.
- BiteCode_dev 5y ago
  Even among devs with very close speciality, the difference in productivity is immense. Take a iOS team, make them dev a MacOS desktop app, and see their output plummet in their of productivity or quality. They will end up doing a good job, but it will take between 6 months and a year to catch up with a specialized team. A data scientist is not even a somebody trained as a programmer. Their strong suit is data analysis, and it turns out one of the tool to manipulate data today are programming languages so their do it. But I as a Python trainer, I train data analyst regularly, and they don't have a clue about language ecosystems, how the OS work, data formats or reliable software architecture. They mainly want to output their graph, pdf report or other media to serve their conclusion. They may want to create some reusable algo, or machine learning model, but that's the limit most of them hit. If one take their code and put it in prod (which I know happens, don't get me wrong), that's not the data scientist fault. They are doing their job, in which programming is just one of the many means to an end, and is not their specialty.
  - jstx1 5y ago
    > But I as a Python trainer, I train data analyst regularly, and they don't have a clue about language ecosystems, how the OS work, data formats or reliable software architecture. This is like teaching some JavaScript to complete beginners at a bootcamp and then declaring that front-end developers aren't real programmers because they know so little.
    - BiteCode_dev 5y ago
      Oh no, I train frontend developers, and they know a lot more than data scientists when it's about programming. I'm not talking about complete beginners. I don't train beginners.