Is “data scientist” the new “programmer”?
Something about this article strikes me as a thinly-veiled complaint about poorly designed object-oriented systems. Take, for example this comment by the author:<p>>Even if the money were half of what today’s coder gets paid it might still be a better job because one is spared the tedium of looking at millions of lines of Java that do almost nothing!<p>What all those millions of lines of code are is abstractions, decoupling, and modularization of logic/responsibilty. This is hard-won knowledge from the field of software engineering. Granted, a lot of it is probably very poorly designed or organized. But the problem is the design and not the philosophy.<p>Because scientists all use the same basic rules of math, but each business will each have it's own special rules (i.e. not all payroll software implements the same policy/axioms), this makes it really easy for the hard logic of scientific work to be in a general-purpose library. "Normal" developers need to customize their own rules, or in other words, develop their own services unlike the data scientists.<p>Now if every data-scientist had to roll his/her own version of numpy, pandas, sci-kit learn, tensorflow, etc. the author would probably be decrying the deluge of procedural spaghetti produced by data scientists. The data scientists' notebooks look simple because of all that indirection is hidden away in the many libraries.