I applaud your approach for a volunteer effort at helping to pull up your successors. I think your approach on throwing them into the pool and being ready to throw a life ring after them was well considered given the constraints all around.
It's hard to think of a field (classical Greek literature?) where grad students aren't going to have to cope with ingesting, transforming and presenting data in all its forms even if they don't have to do more statistical manipulation on it than cross tabs. That's an essential skill set. Being able to do it programmatically, rather than through point-and-click will, as you save "change your life."
I've taken three edX courses this year that you could offer as a supplement. They're all free unless you want a gold star for good attendance and passing the tests. BerkeleyX is an undergraduate survey (in a particularly ugly domain-specific dialect of Python) of techniques of data science. MITx is a big-picture survey of the field of data science for those headed toward industrial-scale application, and HarvardX has a survey source of R, in nine courses, from basics through machine learning, with an text in process (https://goo.gl/UiwiuF) that provides a good guide. The lectures are illuminating. The tests are atrocious -- ambiguous questions, answer-bots untested against edge cases, etc., so I can't recommend paying for a certificate, but it will cover the waterfront for any grad student with a quant agenda to be covered.
A word about functions in R.
f(x) = y == y <- f(x)
Caution any of your future students with programming experience this R is simply advanced algebra, functions with arguments. Most people who walk away from R have tried and failed to map it to C and its descendants as an imperative/procedural language.