two characteristics of data science cannot be overemphasized: (a) for data science to be successful, we need to pay a great deal of attention to how we create our data (in terms of both the choices we make in designing the data abstractions and the quality of the data captured by our abstraction processes), and (b) we also need to “sense check” the results of a data science process—that is, we need to understand that just because the computer identifies a pattern in the data this doesn’t mean that it is identifying a real insight in the processes we are trying to analyze; the pattern may
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