I think the article tagline would be better "Domain Specific Languages for data analysis". Fortunately, the article does mention Python which is critical because new people might not recognize just how prevalent Python is for solving data analysis problems after reading this. The great work of the SciPy community has enabled Python to be used for all of the things that Matlab, R, and Julia can do. In addition, Python can integrate easily with these languages, so if you are a data analyst you need to learn Python.
> The great work of the SciPy community has enabled Python to be used for all of the things that Matlab, R, and Julia can do.
As much as I hate R and love Python, this is not entirely true (unless you count rpy2 as part of "Python"). R has many more statistical models and better plotting capability compared with Python. It also has a lot of domain-specific packages (for example, Bioconductor) that are not available in Python.
Though Python doesn't have the library support that R has, it far exceeds what's available in Julie (and,depending what you are looking for, in Matlab as well)
And it depends on what you mean by "library". For statistics packages, yes, python is behind. But for general purpose computing, connecting to databases, and sheer number of packages available, python is way ahead (currently 4097 in CRAN vs. 24775 in PyPI).
Yeah, agreed. I'm a big, big fan of data analysis in Python, but there isn't a full-featured time series analysis library (statsmodels is almost there). R has at least two that I know of (and needed to hook into from Python using rpy2).
