I'm used to work with a lot of proteomics data from mass spectrometry analysis, for clinical applications. In order to enlarge my skills, and improve my data management and refinement, I would learn a programming language to develop my applications, or automated scripts. In your opinion, what will be the best choice ? Scripts would be focused on statistical refinement, and interaction with proteomics databases.
I wouldn't restrict to a single one, but several. As mentioned already for almost all related to statistics R is the best option. But if also doing something else like you said, to generate automated scripts, perl or python will be better options.
And you will find out that you can use a script in a particular language to call and execute other in a different one.
Thank you for your useful answers. As suggested, I think to combine R and Python.
Right now I'm working with several different frameworks and programming languages.
I think that the most important in my job are now R, python, and C++.
For saving big data, is also important understand a bit of a query language like MySQL or postgreSQL.
Finally, it is also important making all these data readable by the scientific community, so I will suggest also PHP/Javascript/Python/HTML for CGI programming.
Even though Python prevents you from dealing with a lot of annoying complications, you don't want to process big data using this programming language. Thus, if you need to perform computations on large datasets, you may prefer C and C++. Nevertheless, Python is a powerful language characterized by a vast availability of scientific libraries. For this reason, I generally develop my tools using a combination of Python (for the I/O) and C++/CUDA (for the high performance calculations)
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