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I do a lot of statistical work, but wouldn't call myself a data scientist. To that end: > I geared the original curriculum toward Python tools and resources,

I do a lot of statistical work, but wouldn't call myself a data scientist. To that end: > I geared the original curriculum toward Python tools and resources, so I've explicitly marked when resources use other tools to teach conceptual material (like R) Why did you choose Python over R? Personal preference, a bent toward Python in the online courses you found, or is Python generally considered the de facto language choice for professional data scientists? I imagine you could tackle these courses with any programming language, but if Python seems to be the way the data science community is going, it would be helpful to know that. Personally, I'm curious because I'm trying to decide if I should pick up Python on the side to supplement the knowledge I already have of R and various other programming languages. Also, thanks for putting all this together. It's great!

algebr 13y ago
Apparently the R people realize Python over R already.. http://www.r-bloggers.com/the-homogenization-of-scientific-computing-or-why-python-is-steadily-eating-other-languages-lunch/ http://www.r-bloggers.com/the-homogenization-of-scientific-c...
skadamat 13y ago
In most tech companies, data scientists have adopted python over R or Matlab. The benefit is it's easier to scale python to larger datasets, and of course easier for the engineers in the company to take python code that computes algorithms and such and put them into production / products.
hadley 13y ago
Python is definitely not the de facto choice. Python and R both have strengths and weaknesses, and relative use depends quite a bit on community. A few recent surveys (http://blog.revolutionanalytics.com/2014/01/in-data-scientist-survey-r-is-the-most-used-tool-other-than-databases.html http://blog.revolutionanalytics.com/2014/01/in-data-scientis..., http://www.kdnuggets.com/2013/10/rexer-analytics-2013-data-miner-survey-highlights.html http://www.kdnuggets.com/2013/10/rexer-analytics-2013-data-m..., http://blog.revolutionanalytics.com/2013/09/top-languages-for-data-science.html http://blog.revolutionanalytics.com/2013/09/top-languages-fo...) show strong growth for both R and python, and I expect that will continue in the future. There's no reason to limit yourself to one language, and knowing both R and python can only help. That said, you do need to be carefully about spreading yourself too thin, and you want to make sure you're an expert in at least one data analysis environment.
- pyoung 13y ago
  And lets not forget SAS, Matlab/Octave, Julia, STATA, SPSS, etc... There are a ton of choices out there. While not currently in vogue, there are a lot of SAS/STATA/SPSS jobs out there. Generally it is bigger, more established companies that use these software/languages, but if your goal is to get into a more stats focused position, these languages can be a good choice to learn.
clarecorthell 13y ago
Working with a technology that is more highly documented is better when you're starting out. Especially when you're teaching yourself and StackOverflow is your TA. Python is very appropriate for someone who's new to data work. Otherwise, asking what technology to use is like asking what mode of transportation to use to get to a destination -- it's not the point. The important part is that you arrive. Some days walking over the mountain is the least sensible method, other days high seas make taking the boat around it impossible. The tool that gets the job done is the best tool.

algebr 13y ago

Apparently the R people realize Python over R already.. http://www.r-bloggers.com/the-homogenization-of-scientific-computing-or-why-python-is-steadily-eating-other-languages-lunch/ http://www.r-bloggers.com/the-homogenization-of-scientific-c...

skadamat 13y ago

In most tech companies, data scientists have adopted python over R or Matlab. The benefit is it's easier to scale python to larger datasets, and of course easier for the engineers in the company to take python code that computes algorithms and such and put them into production / products.

hadley 13y ago

Python is definitely not the de facto choice. Python and R both have strengths and weaknesses, and relative use depends quite a bit on community. A few recent surveys (http://blog.revolutionanalytics.com/2014/01/in-data-scientist-survey-r-is-the-most-used-tool-other-than-databases.html http://blog.revolutionanalytics.com/2014/01/in-data-scientis..., http://www.kdnuggets.com/2013/10/rexer-analytics-2013-data-miner-survey-highlights.html http://www.kdnuggets.com/2013/10/rexer-analytics-2013-data-m..., http://blog.revolutionanalytics.com/2013/09/top-languages-for-data-science.html http://blog.revolutionanalytics.com/2013/09/top-languages-fo...) show strong growth for both R and python, and I expect that will continue in the future. There's no reason to limit yourself to one language, and knowing both R and python can only help. That said, you do need to be carefully about spreading yourself too thin, and you want to make sure you're an expert in at least one data analysis environment.

pyoung 13y ago
And lets not forget SAS, Matlab/Octave, Julia, STATA, SPSS, etc... There are a ton of choices out there. While not currently in vogue, there are a lot of SAS/STATA/SPSS jobs out there. Generally it is bigger, more established companies that use these software/languages, but if your goal is to get into a more stats focused position, these languages can be a good choice to learn.

clarecorthell 13y ago

Working with a technology that is more highly documented is better when you're starting out. Especially when you're teaching yourself and StackOverflow is your TA. Python is very appropriate for someone who's new to data work. Otherwise, asking what technology to use is like asking what mode of transportation to use to get to a destination -- it's not the point. The important part is that you arrive. Some days walking over the mountain is the least sensible method, other days high seas make taking the boat around it impossible. The tool that gets the job done is the best tool.