Secret Paragraphs from HBR’s Analytics

A prime example of this phenomenon is in analytics.Sloppy nonsense or stellar storytelling?Unfortunately, relatively few analysts are the real deal — buyer beware: there are many data charlatans out there posing as data scientists..If a decision-maker is in danger of being driven to take an important action based on an inspiring story, that is the Bat-Signal for the statisticians to swoop in and check (in new data, of course) that the action is a wise choice in light of assumptions the decision-maker is willing to live with and their appetite for risk.The true nature of a statisticianWhat most people don’t realize is that statisticians are essentially epistemologists..If dealing with statisticians seems exhausting, here’s a quick fix: don’t come to any conclusions beyond your data and you won’t need their services..Unfortunately, because of the differences in coding style and approach between analytics and ML engineering, it’s unusual to see peak expertise in one individual (and even rarer to that person to be slow and philosophical when needed, which is why the true full-stack data scientist is a rare beast indeed).The dangers of under-appreciating analystsWhat beginners don’t realize is that the work requires top analysts to have a better grasp of the mathematics of data science than either of the other applied breeds..Statisticians deal with things outside the data, while analysts stick to things inside it..Researchers typically spend over a decade in training, which merits at least the respect of not being put to work on completely irrelevant tasks.As a result, the right time to hire them to an applied project tends to be after your analysts helped you identify a valuable project and attempts to complete it with applied data scientists have already failed.. More details

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