Depends on the analysis lifecycle I think - I hate using Matlab for writing any sort of large experimental framework in, especially if it has to interact with other systems, but for quick and dirty experiments, plotting, or parallelizing totally independent for-loops eg. for a grid-search over some model hyper-parameters, it's great (the latter is literally changing "for" to "parfor". After you fork out for the Parallel Computing Toolbox of course.) But then a lot of code in research is written in the style of use-code-for-a-week->get a graph->curve is higher than their curve?->publication.
When I need to write something more substantive and reusable though, Python forever.
When I need to write something more substantive and reusable though, Python forever.