this looks suspiciously identical to what software engineering has always been about. There's nothing new in "data science", as per this post, than what has al

this looks suspiciously identical to what software engineering has always been about. There's nothing new in "data science", as per this post, than what has always been true of building a piece of software for non-technical clients. It has always been true that having domain expertise provides a huge boost. It has always been true that requirements are moving targets, that objectives are fluid, that clients don't talk computer science ("data science" in this case). Clients (internal or external) often don't know how to describe their own workflows, and especially edge cases, in rigorous ways. All deja vu. It has always been true that you need to "frame the problem", "clean the data", "design and apply the algos", "communicate the results". We've been grappling with this for 50 years.