As someone who uses Python and R at work, used Matlab in the past, and used SAS, STATA, and SPSS briefly in grad school I found this really interesting. However, and 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".

I realize Python didn't make the cut-off to break out its academic research standing, but I would be curious to see its growth.

In terms of my personal impressions (could be wrong of course, just my feeling/experience) of each that I've used:

SAS - Arcane and annoying. I am happy to be in a position where my group has no interest in using it. I assume it made sense when other packages couldn't handle enormous datasets, but there are plenty now that can. Assume institutional inertia is keeping it in place, particularly at the federal level.

STATA - For fields that previously relied on SAS, seems to be replacing it to some extent. Younger econometrics people in particular. I didn't use it much beyond the basics, but my healthcare economics PhD candidate roommate swore by it. Purpose-driven software.

SPSS - I once had an economics professor refer to it as "a toy for kindergarteners." I kind of view it as a more statistically-focused Excel for people who need a bit more power but can't be bothered to learn any coding. Too in-between for my uses. Seems to be thoroughly entrenched in the social sciences, psychology in particular.

MATLAB - My first introduction to non-excel graphing solutions, and totally blew my mind in that regard. I don't have an engineering background, and I didn't used it for those specific functions. The first programming-type thing I stuck with consistently in my life, and I honestly enjoyed it. I found working through data and statistics in MATLAB to be an utter joy compared to slogging through Excel. Specifically, the customization available in quickly manipulating data and figures.

Python - Transitioned into Python (mostly scipy, numpy, statsmodels, pandas, matplotlib) when I got my current job as we need to quickly iterate and be able to test a bunch of different things. Used the Python(X,Y) suite as a crutch for a while, but now I enjoy trying to find ways to slim things down with "stock" Python. I'm certainly not a great (or even good) programmer, but it's nice to be able to execute any random idea I have over the course of a single Saturday. Seems to have the most flexibility and power out of everything I've used due to its nature as an actual general programming language. Sometimes library support can be spotty for specific statistical operations. I love Spyder, as having a simple variable explorer is something I was really going to miss coming from MATLAB.

R - First thought, "Wow, everything's been done before!" In a day (my first day ever using R) I had a bunch of annoying regressions and analysis up and running, which had taken me more than a week in Python. There seems to be examples for everything, and the basic R package includes a tremendous amount of statistical capability. I prefer Python, as I feel I'm getting more general personal development out of it, but I use R to quickly spot-check any results from Python or Excel.