The NSE for mutate() (and most if not all of dplyr) at this point is all in rlang, which is discussed elsewhere and I am definitely a fan of. If you haven't yet, I definitely recommend checking out the programming with dplyr vignette. As one who programs with dplyr with some degree of regularity, I definitely prefer tidyeval to the previous (lazyeval) implementation (which I preferred still to most of the base R stuff).
One important point that has not been noted is that dplyr now dispatches its verbs against back-ends other than local tibbles / data.frames. (i.e. dbplyr, sparklyr, and the like). So although a database back-end may not be important to the student at present, it most certainly will be in research/industry. +1 for dplyr solutions as a result!
local_tibble %>% mutate() is great, but I love that I can execute on a database by using database_tibble %>% mutate(). More reading on that, if you're unfamiliar.