Data Science, let’s break this down into components:

Code: iterative process (among others), thanks to SLDC, Agile, Kanban, Lean, etc., help guide teams to refine software as intellectual property, and the results are probably managed in Git repositories (or some other versioning system)
Data: it accumulates (in vast quantities, as we’ll see below) while we curate it, prep it, analyze it, monetize it, audit it, etc.
Models: generalizations which are structured by the code, yet learned from the data