https://blog.jupyter.org/2015/04/15/the-big-split/
I think Julia was one of the early alternate languages.
http://blog.dominodatalab.com/lesser-known-ways-of-using-not...
http://ipython.readthedocs.org/en/stable/interactive/magics....
See e.g. https://github.com/DELabUW/szkola-letnia-2015/blob/master/za...
Python fans are a bit unnerving, they seem to be more vocal among the similar alternatives R, Octave/Matlab, SPSS. Some people simply prefer Julia for its nice language syntax and its native raw speed, deal with it.
It'd be difficult to list four languages that are more different than those in your list. There's some overlap in the tasks each is suited for, but they're hardly alternatives.
I didn't want to get into this holywar, but god darn it, what kind of half-assed language uses the notation C = A.dot(B) for C = A * B? It's as if the authors of Numpy took just enough classes of Linear Algebra 101 to learn about dot products, and then dropped out of college to pursue their glamorous careers developing their Matlab knockoff.
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