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I think we're talking about different roles. I'm coming at it from just a modeler role and it seems like you are stating more of a full stack within the data s

I think we're talking about different roles. I'm coming at it from just a modeler role and it seems like you are stating more of a full stack within the data science. I took OP remark as if it was just within data analyst in term of analysing the data and not including turning it into some kind of application. As for Python, time series isn't as good in python. I do agree that Python production is good. But I don't believe Python have better analysis packages. I'm in the camp of using programing language for their strength and using Apache Thrift or whatever to tie everything in.

curiousgal 8y ago
> As for Python, time series isn't as good in python Time series isn't as good Linear regression isn't as good Mixed models are awful Good luck fitting a spline without diving into scipy and doing it as an interpolation R packages are almost always accompanied with a rigorous paper and a great vignette, whereas in Python, there are almost no talks of the implementation and just documentation about how you can use the library. Every model we develop has assumptions and shortcomings, R offers the tools to diagnose and examine that, whereas in Python, I get the feeling that it's more like "some smart guys figured out this formula, here's an implementation of it".
- digitalzombie 8y ago
  I agreed, I didn't want come off as a disgruntle statistician and list the weakness of Python. But you're right. I find most Springer, CRC, etc.. books are in R with accompanying R packages. Shumway & Stoffer Time series book comes with the R astra package. Andrew Gelman Bayesian create Stan and their target was R first (rstan). The Sanford people who created lasso and elasticnet created glmnet. The thought that the creators or experts of these subject wrote a book or research paper and then also publish R package to accompany it is reassuring. When it comes to just data analysis, it seem like R is pretty good or better than Python. I think Python is much better for ML stuff such as deep learning and also it seems like Python have better NLP support. Arguably Stanford is very good with NLP and they publish their tool in Java.

curiousgal 8y ago

> As for Python, time series isn't as good in python Time series isn't as good Linear regression isn't as good Mixed models are awful Good luck fitting a spline without diving into scipy and doing it as an interpolation R packages are almost always accompanied with a rigorous paper and a great vignette, whereas in Python, there are almost no talks of the implementation and just documentation about how you can use the library. Every model we develop has assumptions and shortcomings, R offers the tools to diagnose and examine that, whereas in Python, I get the feeling that it's more like "some smart guys figured out this formula, here's an implementation of it".

digitalzombie 8y ago
I agreed, I didn't want come off as a disgruntle statistician and list the weakness of Python. But you're right. I find most Springer, CRC, etc.. books are in R with accompanying R packages. Shumway & Stoffer Time series book comes with the R astra package. Andrew Gelman Bayesian create Stan and their target was R first (rstan). The Sanford people who created lasso and elasticnet created glmnet. The thought that the creators or experts of these subject wrote a book or research paper and then also publish R package to accompany it is reassuring. When it comes to just data analysis, it seem like R is pretty good or better than Python. I think Python is much better for ML stuff such as deep learning and also it seems like Python have better NLP support. Arguably Stanford is very good with NLP and they publish their tool in Java.