Comparing R, Octave, and Python for Data Analysis 61
Here is a breakdown of R, Octave and Python, and how analysts can rely on open-source software and online learning resources to bring data-mining capabilities into their companies. The article breaks down which of the three is easiest to use, which do well with visualizations, which handle big data the best, etc. The lack of a budget shouldn't prevent you from experiencing all the benefits of a top-shelf data analysis package, and each of these options brings its own set of strengths while being much cheaper to implement than the typical proprietary solutions.
Re:Did I seriously miss something? (Score:5, Interesting)
The whole article was not much more than a high level review. The graphic naturally draws attention to the parameters the writer wanted to cover but he did not back up his graphic with any sort of serious textual review of what he felt were the weaknesses or advantages of the different programming language at least not in any detail.
And what he has is flawed as well. For example, he marked R as having issue with big data which is quite wrong - I routinely analyze multi-GB datasets in memory, and my databases go into TB. Of all the three languages R is the only one to have a native format (data.frame) that interfaces easily to database queries. Both Octave (Matlab) or Python have to use compound types which make addressing difficult.
Also, I found R easier to master than either Octave or Python, but this is probably because I am familiar with Lisp.
Or if you can't make up your mind (Score:2, Interesting)
Sage math http://www.sagemath.org/