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It really depends on what you're doing. If you're a developer tapping into data analysis, sure. But I'm building predictive models for a living and I evaluated
by mziel 11y ago
It really depends on what you're doing. If you're a developer tapping into data analysis, sure. But I'm building predictive models for a living and I evaluated quite a few different frameworks and languages.
Sophistic algorithms for missing data, sparse solutions, latent modelling. You can find the many industrial grade algorithms in Python, Spark, Julia, Weka etc. but for stats-heavy data science/machine learning R is unmatched. Sans writing your own implementations based on pseudocode, but this is really not effective use of your time for prototyping.
EDIT:
And even for simple stuff, the strength of R packages is easy to prove since they are either directly ported to other languages (ggplot) or heavily influence the implementation (pandas).
- Lofkin 11y agoWith Pymc3, you can code a bayesian generalization of most of those packages pretty easily. For bayesian models, it is easier than calling into C++ with stan in R. For everything else, one can call arbitrary R packages with Rpy2, albeit with clunkier syntax. For agent based modeling, there is no comparison. Python with its better and faster class system and Numba is in another league. Finally python has distributed and out of core lists, arrays and dataframes: http://dask.pydata.org/en/latest/ http://dask.pydata.org/en/latest/