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R ... The language is just a joy to use. * Great environment with RStudio * Awesome plotting and visualization with ggplot * Very welcoming community with a

R ... The language is just a joy to use. * Great environment with RStudio * Awesome plotting and visualization with ggplot * Very welcoming community with a very diverse background ranging from social science to quantitative finance Basically for everything except deep learning R is a great alternative to python. If you rely on advanced statistics I think R is the way to go.

cdcrabtree 8y ago
I'd second the R recommendation. It's a particularly exciting time to use R, with the proliferation of package collections like tidyverse (https://www.tidyverse.org/ https://www.tidyverse.org/) and the development and release of free texts like R for Data Science (https://r4ds.had.co.nz/ https://r4ds.had.co.nz/).
confounded 8y ago
It is still the best language for exploratory data analysis, fitting models intuitively, and visualizing data. Being able to do these things thoroughly and quickly, has (in my experience) been the most important technical skill for getting data science right, regardless of the language production models end up getting deployed in.
usgroup 8y ago
Definitely R, but IMO from the start learn it with tinyverse and Rcpp. I.e use it as a pipelining and glue language but don’t write any processing code in it. R is slow, and memory hungry. Having said that tinyverse basically calls methods written in C and has a beautiful syntax. As Rcpp let’s you write whatever needs to go quickly in c++ then easily expose it in R.
altairiumblue 8y ago
> R ... The language is just a joy to use. My experience has been the exact opposite. R is great as an interactive statistical environment. But if you need to build data-intensive applications in it - it's still powerful but somewhat slow and also a bit of a mess: many exceptions, special cases, strange design choices and multiple ways to do the same thing. It can get the job done, but there is an unpleasant learning curve to get used to all the weirdness.

cdcrabtree 8y ago

I'd second the R recommendation. It's a particularly exciting time to use R, with the proliferation of package collections like tidyverse (https://www.tidyverse.org/ https://www.tidyverse.org/) and the development and release of free texts like R for Data Science (https://r4ds.had.co.nz/ https://r4ds.had.co.nz/).

confounded 8y ago

It is still the best language for exploratory data analysis, fitting models intuitively, and visualizing data. Being able to do these things thoroughly and quickly, has (in my experience) been the most important technical skill for getting data science right, regardless of the language production models end up getting deployed in.

usgroup 8y ago

Definitely R, but IMO from the start learn it with tinyverse and Rcpp. I.e use it as a pipelining and glue language but don’t write any processing code in it. R is slow, and memory hungry. Having said that tinyverse basically calls methods written in C and has a beautiful syntax. As Rcpp let’s you write whatever needs to go quickly in c++ then easily expose it in R.

altairiumblue 8y ago

> R ... The language is just a joy to use. My experience has been the exact opposite. R is great as an interactive statistical environment. But if you need to build data-intensive applications in it - it's still powerful but somewhat slow and also a bit of a mess: many exceptions, special cases, strange design choices and multiple ways to do the same thing. It can get the job done, but there is an unpleasant learning curve to get used to all the weirdness.