At first glance Julia looks excellent. Glad to see money going to a project that makes sense :)<p>I guess Python is &quot;the competition&quot; so here&#x27;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&#x27;m not sure the &quot;easier&quot; argument is what should be focused on.
At the &quot;lower end of the spectrum&quot; (that is not super hard data crunching and&#x2F;or less affinity for programming) there&#x27;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).<p>The big attraction of Python (for me) is that you don&#x27;t just get the data crunching but also the boilerplate you&#x27;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.<p>If I were to give advice to someone entering science I&#x27;d certainly suggest &quot;learn python&quot;.
However...the more languages we have the better, MATLAB makes me cringe for multiple reasons. Go Julia :D