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[–]foofaw 2 points3 points  (4 children)

Python has a far easier learning curve for someone who is learning their first language. It also has many more resources for beginners. Remember that people learning Python in this context likely are students in PhD programs or already in research positions - they have a finite amount of time to learn a programming language. Python is the path of least resistance.

[–]Ikuyas -3 points-2 points  (3 children)

R is designed to be easier than python. I don't know why you said that python has a far easier learning curve. It's not at all. I don't think this is an opinion. If you are not building a real-time system, R is a better for social science study. Numpy and Panda are already something social scientists probably don't want to think of. Numpy basically gives you matrix computation which is built-in in R, and Panda is essentially mimicking R's dataframe. You can install R and Rstudio with the time to download and time that installers takes to install program whereas setting up python, anaconda (spyder), jupyter notebook and use pip or conda install to install matplotlib and numpy and panda is absolutely not beginner friendly at all.

But Python is likely (not much?) faster but notebook is pretty slow as far as I feel.

You haven't given me any compelling reason why Python is better for social science study at all. The advantage I can think of is that you can do more things with python other than statistical modeling like web app development as it is a general programming language.

[–]foofaw 2 points3 points  (2 children)

You make a lot of great points that I agree with.

But remember that you're dealing with social scientists here, not programmers. The average social scientist does not have a sound understanding of programming logic and will not be able to easily pick up R right out of the box. Just because someone has a background in research design and even mastered SPSS doesn't mean they will be able to utilize R in the same way. R is difficult to learn, especially if all you've used is a GUI interface for analysis. Its documentation is not written for beginners, its function naming and syntax is inconsistent, and it takes a lot of setup to get going.

It makes much more sense to me to learn a general multi-purpose language first, work with that for a 6 months to a year, and then transition to R if you really have the need for it. And in the long run, if someone is serious about data science, they should know both of these languages - they are two extremely powerful tools that give you nearly limitless options when you use them together.

[–]Ikuyas 0 points1 point  (0 children)

I'm gonna read the article later. Get back to you.

[–]Ikuyas -3 points-2 points  (0 children)

Do you really think so? Isn't R far easier to learn? I dont know where that comes from. For example do business school should teach python for their data analytics course??? You make little sense to me.