Data science as the application of theoretical knowledge

Patrick Atwater writes:

Insights that “much of what’s hard looks easy” and it’s about “getting the damn data” highlight important points that much of the tech-ey industry dominating definitions overlook in the excitement about production ML recommendation systems and the like.

Working to build from that grounded perspective, I penned together a quick piece digging into what really defines data science and I think the applied nature of the work that you hint at holds an important key. In many ways, the confused all-things-to-all-people nature echos the fractal nature of a field like “management” which devolves into poetic aphorisms and intellectual-lite books elucidating best practices while at the same time pulling from more formal academic disciplines (civil engineering, environmental science, and chemistry for instance in water management).

Consider an applied data science example. A friend at a water utility I work with built a billing calculator using R shiny. That required something like a half day of analytical work and then a couple weeks to get the UI/UX looking right and the servers playing nicely. Note that’s an analyst doing the work rather than a software engineer which I think speaks to the interdisciplinary nature of data science and the oft cited CS / Statistics / Domain expertise venn diagram.

I don’t really have anything to say about this—the language is too far from mine—but I thought I’d share it with you.

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