Picking an Analytic Platform
Summary: Picking an analytic platform when first starting out in data science almost always means working with what we’re most comfortable. But as organizations grow larger there is a need for standardization and for selecting one, or a few analytic tools.
Picking an analytic platform when first starting out in data science almost always means working with what we’re most comfortable. That in turn almost always means whatever we used in college (or your certificate course) be it R, Python, SAS, SPSS or whatever our instructors directed us to use. After all, we want to be both comfortable and efficient and all other things being equal, that means sticking with what we know.
Before R and Python became the go-to’s on many campuses, SAS and SPSS understood this lesson well and had very aggressive discounts for students and instructors (read that as nearly free). Now that there’s open source that’s been diluted somewhat but the majors keep plugging away with that strategy and it still works.
If there were a good reason to switch we’d consider it. But the reason would have to be pretty compelling, meaning features like easier or broader: