Both languages ​​are good for data science. My opinion is take your time to learn one in depth. The best language is one that you know how to use.

I use only Python. A lot of useful package for scientific computing.

Happy click-baiting;)

Volodymyr Brazhenko Yes of course...

I use both...

In my opinion R is more high-level compared with Python.

but i like working with python because of it's popularity, communities, packages and specially it's wide and powerful application in big data. ( such as PySpark )

but R also is powerful same as Python specially in visualization.

Jochen Wilhelm Thanks ;)

Mohammed Deeb

Pritam Kumar Panda

Both are great for data science but one is better than other in some special cases. Start with R, shift to Python gradually and then start using both.

Safa Aouinti Thank you Safa... also thanks for your useful link

Saikat Das Thanks for your suggestion... yes, it's a good way

Personally, I use both of them as each of which has its own pros and cons. I use python for data processing, reduction and machine learning analysis.

However, I use R in parametric statistics, and for data and results visualization.

In the end both of them are free tools, developed from C language and can be used in data analysis.

Taqwa Alhadidi Thanks.

yes it's a good idea to use both of them based on needs.

I agree with this opinion, as same as another.

Both languages ​​are good for data science. My opinion is take your time to learn one in depth. The best language is one that you know how to use.

This is good paper:

Article Using R in hydrology: a review of recent developments and fu...

Elpidio Inacio Fernandes Filho Thanks for your reply... yes your opinion maybe is the best idea.

Mahboobeh Kiani-Harchegani Thanks for article recommendation

i'm using python because it is very simple and powerful

Alhasan M. Sayed is Python more simpler that R?

When I do statistical research, I prefer to use R for the academic reference and trust-worthy statistical algorithm. When I need to process "big" data, I prefer to use python for its wide-ranging packages, data process ability on wider data types (such as string) and speed.

So it depends on your need. I prefer to combine them together.

Yuan Chen yes, similar to my opinion...

Yuan Chen Since you have experience with R and python both, can you be precise what exactly R cannot to what python can? I recently started to dig into the tidyverse and would like to know how far I can get with it before being "forced" to learn python :)

Thanks in advance

Holger

Hi, Holger

The main advantages of Python focus on speed and data process ability. Python can process more than 1 G data. Only R hardly processes on these quantity smoothly and you have to use SQL/HIVE/HADOOP together with R.

Some of python's packages (numpy, Pandas) is written by C or from Intel Math Kernel Library etc. These ensure the processing speed.

In all, Python may have a higher industrial ability. R may have a higher academic ability.

Yuan Chen

Yuan Chen and R is more high level than python so learning R programming is a bit simpler in our opinion.

Holger Steinmetz

Hi Holger.

i'm not experienced in R as same as Python, But I know R by it's simplicity. although Python is a bit complicate but not as much as another programming languages such as Java, C, C++, C# and so on.

As Yuan Chen pointed in previous comments, on of the python's excellence relative to R is application of Python in Big Data Analysis where PySpark comes up and also application of Python in Map Reduce Programming for distributed file systems such as Hadoop.

But i do agree with Yuan in this subject that R is more powerful in statistical analysis.

Best Regards

Masoud Sadeghi That helps. I don't think that we as social scientists often have hadoop based data, or am I wrong? So R may be sufficient?

Best,

Holger

Holger Steinmetz you are a social scientist and a person like you at least should understand Big Data Concepts like Hadoop, etc. Data Velocity, Data Variety , Data Volume and such another characteristics of data is a serious challenge. exploration and research in Big Data field and applications is critical now in every data applications.

Notice that it is just my opinion and you can search more for better results and whatever you want. :D

Good Luck

Both these languages are good enough for Data Science. So here are a few points that might help you in choosing between the two -