hey kai,
Need your advice, as you have more experience in data science.
Which is more used in real world application R or Python ?
I know python and I want to learn data science using python.
Would I have to learn R ?
thanks,
Savan
My response:
Need your advice, as you have more experience in data science.
Which is more used in real world application R or Python ?
I know python and I want to learn data science using python.
Would I have to learn R ?
thanks,
Savan
My response:
Hi Savan,
Regarding your question, there is no right/wrong answer in deciding whether it is necessary to pick up a new language. There are many arguments out there (http://insidebigdata.com/2013/12/09/data-science-wars-python-vs-r/). Since I started R first (before R I got some experience in MATLAB), I am primarily an R user and it's my current tool for data processing and analysis. My overall experience with R is that it is very flexible, meaning there are many available packages (and getting more and more) that are developed and can be applied in many fields; but I heard people arguing there are so many tricks in R (i.e. shortcut functions) that could make the codes kind of messy. One strength of python, though I don't have an intuition yet, is it's highly scalable (able to handle small or huge data set), and I guess this is part of the reasons python is very popular in machine learning.
So, it's useful to know another language for data science, but it is also necessary to be realistic and figure out the time and the study load that one might take, since it's also a learning process. My suggestion would be stick to python first, but feel free to explore. After all, the core of data science is to "get hands dirty" (http://www.kdnuggets.com/2015/05/data-science-inconvenient-truth.html), so there will be many opportunities to practice; as the hands get more dirty, one may need to come up with new solutions to the problems, and at that point R might come in and play.
I hope this help.
Best,
Kai
Regarding your question, there is no right/wrong answer in deciding whether it is necessary to pick up a new language. There are many arguments out there (http://insidebigdata.com/2013/12/09/data-science-wars-python-vs-r/). Since I started R first (before R I got some experience in MATLAB), I am primarily an R user and it's my current tool for data processing and analysis. My overall experience with R is that it is very flexible, meaning there are many available packages (and getting more and more) that are developed and can be applied in many fields; but I heard people arguing there are so many tricks in R (i.e. shortcut functions) that could make the codes kind of messy. One strength of python, though I don't have an intuition yet, is it's highly scalable (able to handle small or huge data set), and I guess this is part of the reasons python is very popular in machine learning.
So, it's useful to know another language for data science, but it is also necessary to be realistic and figure out the time and the study load that one might take, since it's also a learning process. My suggestion would be stick to python first, but feel free to explore. After all, the core of data science is to "get hands dirty" (http://www.kdnuggets.com/2015/05/data-science-inconvenient-truth.html), so there will be many opportunities to practice; as the hands get more dirty, one may need to come up with new solutions to the problems, and at that point R might come in and play.
I hope this help.
Best,
Kai
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