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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The way we talk about data science and focus so much on methods, we actually incentivize working with *bad* data, rather than spending the time to collect good data and then use easy methods with it
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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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