I guess Python is "the competition" so here's my thoughts on that competition (from knowing fairly little about Julia). From my limited experience Python is actually easy enough to grasp for most scientists without a programming background so I'm not sure the "easier" argument is what should be focused on. At the "lower end of the spectrum" (that is not super hard data crunching and/or less affinity for programming) there's social sciences, psychology, marketing, economics etc. and they use a variety of higher level tools (the pesky SPSS) for their routine tasks and are usually willing to invest time for more data crunchy activities (often R but Python is common as well in say economics with data-crunchy models).
The big attraction of Python (for me) is that you don't just get the data crunching but also the boilerplate you'll need around it (data normalization, stroing stuff in databases and getting it out, turning your models into simple web-apps etc.) + IPython notebook and distributions like anaconda make setup and quick experimention fairly simple.
If I were to give advice to someone entering science I'd certainly suggest "learn python". However...the more languages we have the better, MATLAB makes me cringe for multiple reasons. Go Julia :D