>I realize this might be difficult to account for, a lot of analytics actually just happens in Excel. For quick and dirty statistics or basic linear regressions

>I realize this might be difficult to account for, a lot of analytics actually just happens in Excel. For quick and dirty statistics or basic linear regressions, Excel represents a widely known quantity. In particular, I've been stuck using Excel in the past just because the final product is heading off to an individual or group for whom anything else is "too technical". As someone who has/can use any of the listed software, another benefit of Excel (in addition to its pervasiveness and popularity with "non-technical" people) is its rapid prototyping capability. If your data is small-ish and requires no serious stats calculation (which accounts for a lot of data analysis, especially in the early phase), Excel is super fast to prototype in. You can do basic math, aggregation & filtering and charting all within a few keystrokes. Another benefit that I see is that as an interface, spreadsheet shows you to show each step of the work. As a programming environment, spreadsheet is just a mess, but I find it easier to track what I am doing in Excel than working inside R/Python REPL and staring at my history or currently defined variables.