Row 4887

Row ID: 4887 | Dataset Entry | Axioma AXP Content Repository

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I've been working as a DS in predictive analytics a good amount of years now, and recently I've been pushed to dig a bit more on the more data viz side, which eeeehh fortunately or unfortunately meant coding web-dev stuff. I have realized that in the web-dev side, there are a shit ton of people building and consuming boilerplates. Like for real, a mind-blowing amount of demand for such things that I would not have expected in my life.

However, I've never seen anything similar for DS projects. Sure, there's decent documentation and examples in most libraries, but it's still far from the convenience of a boilerplate.

Talking to a mate he was like, I'm sure in web-dev everything is more standard than DS, and I'm like... man have you seen how many frameworks, backends, styling clusterfuck of technologies is out there. So I don't think standardization is the reason here. Do you guys think there is a gap in DS when it comes to this kind of things? Any ideas why is not more widespread? Am I missing something all toghether?

EDIT: By boilerplate I don't mean ready to go models, I mean skeleton code for things like data loading and processing or result analysis, so the repetitive stuff... NOT things like model and parameter turning.

FieldValue
text I've been working as a DS in predictive analytics a good amount of years now, and recently I've been pushed to dig a bit more on the more data viz side, which eeeehh fortunately or unfortunately meant coding web-dev stuff. I have realized that in the web-dev side, there are a shit ton of people building and consuming boilerplates. Like for real, a mind-blowing amount of demand for such things that I would not have expected in my life. However, I've never seen anything similar for DS projects. S…
label r/datascience
dataType post
communityName r/datascience
datetime 2024-04-23
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{
  "text": "I've been working as a DS in predictive analytics a good amount of years now, and recently I've been pushed to dig a bit more on the more data viz side, which eeeehh fortunately or unfortunately meant coding web-dev stuff. I have realized that in the web-dev side, there are a shit ton of people building and consuming boilerplates. Like for real, a mind-blowing amount of demand for such things that I would not have expected in my life.\n\nHowever, I've never seen anything similar for DS projects. Sure, there's decent documentation and examples in most libraries, but it's still far from the convenience of a boilerplate.\n\nTalking to a mate he was like, I'm sure in web-dev everything is more standard than DS, and I'm like... man have you seen how many frameworks, backends, styling clusterfuck of technologies is out there. So I don't think standardization is the reason here. Do you guys think there is a gap in DS when it comes to this kind of things? Any ideas why is not more widespread? Am I missing something all toghether?\n\nEDIT: By boilerplate I don't mean ready to go models, I mean skeleton code for things like data loading and processing or result analysis, so the repetitive stuff... NOT things like model and parameter turning.",
  "label": "r/datascience",
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}
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