[trailrunner46]

Python is certainly more popular and for job prospects I always tell that to newer data folks. That being said if you want to load in some data do some SQL like manipulation, run some stats and make a graph or output a report I would argue R is way better experience than Python but that’s much more about the package ecosystem and less a comment on the language. Dplyr is just more friendly to use than pandas (often 3-5 ways to do something and as a beginner this can be disorienting) and ggplot2 vs matlibplot. For interactive graphs you are probably going to use plotless anyway from both languages.

One other thing I would mention is knowing SQL well is the most translatable skill. A lot of dplyr and pandas are doing SQL like operations (in fact dbplyr will generate SQL equivalent commands for your dplyr code for various backends).

In summary know how to manipulate data in SQL then pick a language (because you will need to do some IO/reporting stuff outside just data work) where the ecosystem of packages feels user friendly to you and your work flow and roll with that.

[scottmcdot]

These days I usually prefer to use sqldf. It's so powerful how it let's you reference existing dataframes within the SQL command. It's like spark.sql in Python and temporary views but much more simple.