To be a mature data analyst, you must also be a programmer:
One additional general point needs emphasis: in modern computing, there should not be a sharp distinction between users and programmers. Most programming with statistical systems is done by users, and should be. As soon as the system doesn’t do quite what the user wants, the choice is to give up or to become a programmer, by modifying what the system currently does.
Such user/programmers then naturally go through stages of involvement. In the first stage, the user needs to get that initial programming request across to the system, quickly and easily. Later, the user needs the ability to refine that first request gradually, to come closer to what was really wanted. Good software for computing with data should support all such stages smoothly.
From "Computing with Data: Concepts and Challenges" (1998) by John
Chambers - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.419.1750
Later in the same paper:
It may seem obvious, perhaps even trivial, to assert then that languages, and in particular good languages, are the heart of successful computing with data. I believe the statement to be true, but it is not easy to defend explicitly, and much activity in computing with data (or equally in other kinds of computing) goes on as if it were not true.
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