I had a bit of a breakthrough in terms of my thinking of data science thanks to all the interesting discussions at #rstudioconf2020 --
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I want us to have a conference solely focused on how people collect data, and all the politics / product negotiations / etc that go along with that
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One small thing I am doing at Stitch Fix -- for the datasets we use, I'm referencing them as e.g. "the data that Cindy, Ping and Francesca created" rather than "the stylecard data".
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This is a false dichotomy. In an ideal world, data collection informs the method, and the choice of method might influence data collection. And in reality, one usually has more choice over method than over data collection.
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I don't disagree that it is a false dichotomy. I think data scientists in general have much more influence than they might think, but it just takes more time and politics than most will tolerate
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Agreed. Spending time on dealing with data quality problems and calculating better, more relevant features will almost always contribute more to the predictive power of a model than tuning the model or increasing its complexity.
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Part of the problem is data cleaning is seen as “less than”. But I know the scientific decisions that are forfeited if you just let some low-pay lackey do it. Oh wait that’s me.
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I agree there's over-hype around cool methods and not enough thought about the data generating process that our sampling scheme should be capturing. But fancy methods are necessary too because accurately capturing the process is often infeasible (cost, ethical constraints, etc)
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Oh for sure, no doubt about that. But for many tech applications specifically, the juice is not worth the squeeze
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That's starting to sound like putting "science" into "data science".
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it's funny bc I talked at a plant pathology conf recently and was like "I guess I don't have to tell you all to care about the underlying question"
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