Seriously you do not want me to join you. There are three main reasons for this.

  1. Data science is not science. It is not a field of inquiry or topic area of research in science. 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. So called data science is not science or even a science, it is a tool of science. A useful and powerful tool to be sure but in the end science could and did get along just fine before the advent of the age of “data science.” 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. They are still only tools of science just like so called “data science.” Please stop using that terminology. It is a tautology at worst (science is science) or misleading and confusing at best. 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 I hope the point of the example is clear. A tool of a thing is not the thing itself and never can be.
  2. There is no such thing as machine learning. Machines cannot learn. By the very definition of each word used in the term it is a logical contradiction and therefore nonsense. Please see the following for details.\
  3. There is no AI, nor type of AI and it is not “just around the corner” or coming any day now” nor is it or will it “revolutionizing/ize the field of (fill in the blank) because it does not exist. Will it exist one day? Perhaps in 500–1000 years, tomorrow sadly, except in the fevered imaginations and masturbatory fantasies of the tech utopians, the answer is no.
  4. Machine learning, AI, neural networks, and nanofillintheblank are the top 4 most overhyped, overused, least well understood, overinvested in, least useful terms, of the past 10–15 years. Data science is rapidly moving into the top 10.

I have written exhaustively on each of these topics and will continue to do so until some level of sanity and balance is restored to the universe.

All that said if you still want me to join and will have me, count me in! Someone needs to keep you a-holes in check and there is nothing I enjoy more than disabusing some data nerd of their most cherished but clearly erroneous notions.

Written by

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

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