[ced]

Well, what do you work on? Julia isn't for everyone.

I use it because it has quite good numerical primitives, and I can quickly make a slow, Python-like first pass at an algorithm, then profile and get C-like performance in the bottlenecks with minimal effort. And if I need a particular library, I can call Python's. Also: macros and multiple dispatch make a big expressiveness difference for my type of work.

[TheLogothete]

Lifetime values, customer segmentation, lead scoring, customer life cycles, customer attrition and also quite a bit of reporting. Some text analysis. I use R because it offers superb speed of development, extensive documentation, commercial support and many partner opportunities with the likes of Oracle, Microsoft, Alteryx, Tableau, Tibco and pretty much every analytics vendor. In my experience, R's slowness has been greatly exaggerated.

[ced]

Yeah, I would use Python for this kind of task, too. Vectorized operations are fast enough in a lot of cases, and the library advantage is important. At this point in time, Julia is a great C/Fortran replacement, but for Python/R/Matlab, it's a trade-off.