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The author seems to have glazed over quite a bit of the rather mature Python ecosystem simply because it lives in a general purpose language.
by freyrs3 14y ago
The author seems to have glazed over quite a bit of the rather mature Python ecosystem simply because it lives in a general purpose language.
- mwexler 14y agoPerhaps, though he states "In this area, it’s unfortunately hard to dispute that general-purpose scripting languages like Perl, Ruby, and Python, have much better language and library support in this area than any of the data-specific languages." It's also highly likely that most analysts reading this article know lots about Python (and Ruby), some about R, and little to nothing about Matlab, Octave, or Julia, unless they have an engineering background. (Yes, some analysts do have backgrounds other than CS/Eng/Physics!) So, I don't think it's glazing as much as it's trying to focus on the new and not the "yet again, someone telling me how to use Python for data, when I want to know if there's something even better...".
- davemaya 14y agoI also have the impression that the research that went in this article is a quite light. R, ML, Python serve different purposes. Julia is cool but brand new and feel kind of out of place compared to the heavy weight that are the other three. The point the author makes about ML and R being black boxes is misleading. At least, R is open source and you can always look at the gut of the software, nothing is more easy.