I recently had similar thoughts. Here’s my biased view. The more I learn about ‘data science’ the more I think the term is dangerously misleading and shows a misunderstanding of the nature and practice of scientific thinking. Somehow marketers and business media sold us on the idea that people with expertise in the methods of generating the most timeless and universal knowledge using collections of carefully chosen and precise measurements processed using computers would be well-suited to generating actionable business advice based on often arbitrarily chosen and noisy measurements. Data ‘science’ as it is practiced in probably all the major companies outside the Big 4 is very nearly an oxymoron. Yet because the pay is so good I think most are able to deal with the cognitive dissonance. From the industry uses of “data science” I have seen, at best the conclusions we can draw are very narrow, limited to very specific geographic and temporal contexts, and with very wide error bars attached to them. There is little to no attention paid to measurement and sampling bias. It doesn’t matter how much data you have if you’re measuring the wrong thing or the wrong part of the thing. Unfortunately most data collection systems were designed to ease accounting questions, not to answer metaphysical questions about the nature of a phenomenon. At its most helpful it’s clever guessing, and at its least it’s potentially dangerous. Whatever ‘data science’ is as currently practiced in industry, it doesn’t seem to exhibit most of the features we would normally associate with any kind of scientific inquiry (see Popper’s “demarcation criteria” debate in the philosophy of science). So let’s just change the name to “data wrangler” or “data whisperer” or “analyst” whatever better reflects the goals and purposes of such a job. But drop the pretense that what most do is actually “science.”

Travis Greene. PhD Student in Business Analytics at the Institute of Service Science, National Tsing Hua University https://greenet09.github.io/datasophy/

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