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>&gt;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&#x2F;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&#x27;s own special rules (i.e. not all payroll software implements the same policy&#x2F;axioms), this makes it really easy for the hard logic of scientific work to be in a general-purpose library. &quot;Normal&quot; 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&#x2F;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&#x27; notebooks look simple because of all that indirection is hidden away in the many libraries.