It’s a good thing I don’t need data scientists because there are no such thing. There is no such thing as data science either. Just because most people call data ‘science’ science does not make it so. It is not science or even a type of science, if a ‘type’ of science were indeed possible. (there are fields and subfields of science but ‘data’ is not one of them). Something is either science or it is not, and data science is not. It is a tool of science, a powerful tool indeed, but nothing more than that. I am sure the experts in the use of this tool think it is cool and fun to call themselves scientists when their friends and family ask them what they do for a living (I liked it better in the old days when scientists were nerds and nobody wanted to be one says grumpy old scientist, lol), but they need to stop kidding themselves, they are not a scientists. All science is data science in the sense that all science uses parts of some of the tools that fall under the rubric of “data science” to analyze data that is generated by the experiments that are conducted in that particular field or, in the case of purely theoretical fields, to analyze data sets that are relevant to the theory. Statistics and mathematics have always been useful tools for the scientist and they always will be, calling them data science does not make them science. More recently, powerful computers, and clever algorithms have greatly expanded the insights to be gained from even the most mundane of data sets. Moreover, the size of data sets that can be analyzed has expanded allowing for ever more powerful and increasingly accurate predictions (in some cases though not nearly as many as once thought likely). That said, using computers running algorithms to analyze data (no matter how mundane and small or how interesting and large) is not science, computers are still only tools of science and thus not science. A good, though imperfect analogy is a calculator in mathematics. It is a very useful tool for doing math, yet there is no field of “calculator mathematics.” Yes the analogy fails in many respects but the point of the example is clear, a tool of a thing is not the thing itself and never can be.

Like machine learning, my favorite logically impossible logical contradiction, data science joins the pantheon of technology terms which are absurd, nonsense, wrong, impossible, non-existent, and/or without set meaning (can mean whatever anyone wants it to mean), but which scads of people continue to use on a daily basis totally oblivious to that fact. Then again, who’s to say what’s wrong or right, sense or nonsense, absurd or surd, in this wacky world we live in today? What with AI’s on every street corner and machine’s busy learning away left and right. I have an idea, let’s ask the data scientists, they’re scientists so they must be smart. Of course not nearly as smart as AI, let’s ask them. But which one shall I ask? So many to choose from out of the zero that currently exist.

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Research scientist (Ph.D. micro/mol biology), Thought middle manager, Everyday junglist, Selecta (Ret.), Boulderer, Cat lover, Fish hater

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