Customer Review

Reviewed in the United States on May 18, 2017
although billed, at least in part, as aimed at "business people who will be working with data scientists, managing data science-oriented projects, or investing in data science ventures" (p xiii), the book never points out that all analytic techniques make assumptions and that the data scientist needs to be questioned about that (when they don't mention it upfront) and questioned about what happens when assumptions are violated; in addition, many, maybe most, techniques have biases and these are never mentioned either; there is also no discussion of bootstrap (the authors use cross-validation instead thus, generally, wasting information) or of external validation and no warnings about what to beware of when using surrogates; at a lower level, the book is generally readable and generally well-informed but needs to be supplemented with something that covers how to, at least, question the technical people about assumptions and biases
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