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by tanilama
2687 days ago
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> I can say that static typing is desired by a nonzero number of who practice what we would consider to be data science and machine learning Who would trade static typing with fast prototyping any time. Data science is a really nebulous term covering many drastically different domains of CS. Many DS I talked with, don't really produce code, they do coding to produce analysis, which is the actual delivery. For them, code is ad-hoc and disposable, created on demand and left in the dust until rediscovered when mission comes. Some of the code do survive and enter production stage, I guess that is where they would seek some assurance from static typing. But I do think they could learn to mitigate most of pain if they can commit themselves to write some unit-tests/functional tests, yet such awareness is rare among the DSs I know and worked with. So all in all, yes static typing MIGHT help, in some way, but I don't think it addresses the underlying pain point as much. |
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These need not be at odds. Many ML languges like F# or OCAML, by use of type inference, get you type safety without having to type a bunch of stuff and sacrifice faster prototyping. And certainly in F# there is a history of having productive tooling that lets you prototype easily. Simply writing some F# code in an F# script in an IDE, hitting alt+Enter, and letting it execute in an interactive shell is hugely productive for exploratory tasks. And features like Type Providers build out types for an arbitrary data set that let you guarantee your code is actually correct for the data.
What I've mentioned isn't without its flaws, and eventually someone is going to reach head-scratching problems just as they would in any other environment. I don't think there's an objective way to measure productivity across a wide range of professionals, but I do believe that some subset of them would prefer static types for their work. This is backed by conversations with some of them about problems they encounter.