BOL

I recommend USA. Study bioinformatics in U.S. universities and colleges may differ from those in your home country in several ways. For one thing, competative research lab, small class sizes are very common. There may be as few as 10 to 20 students in a class, giving you the personal attention you need in order to succeed. While in class, students are encouraged and expected to contribute to the discussion. Studying in the U.S. gives you the opportunity to gain a mentor in your given career field, an invaluable resource.

GENOMICS AND COMPUTATIONAL BIOLOGY 

https://genomicscomputbiol.org/ojs3/GCB/index

I would recommend python and lower level language C because of easier syntax and simple oops approach. Moreover, I do a lot of boinformatics programming, but I don’t use any of the “Bio*” projects, as they never seem to have much that is of use to me, and what little is useful to me is easier for me to code myself than to install any Bio* project and deal with its idiosyncracies.

Read this report:

http://qz.com/298635/these-programming-languages-will-earn-you-the-most-money/

Python is beating all others https://towardsdatascience.com/the-most-in-demand-skills-for-data-scientists-4a4a8db896db

R is an exceptionally powerful language in manipulation and transformation of data, statistical analysis and graphics. It doesn have support for the wide array of statistical functionality. So in my opinion R is great if all you're doing is statistics. It's got a nice interactive interface and visualization tools.

R discussion http://bioinformaticsonline.com/discussion/view/119/which-are-the-best-statistical-programming-languages-to-study-for-a-bioinformatician

You can also found several other material at http://bioinformaticsonline.com/groups/profile/93/r-and-bioconductor

My vote goes to R (http://cran.at.r-project.org) environment is an easy to use and write programs and custom functions. The R programming syntax is extremely easy to learn, even for users with no previous programming experience.

R has also quickly found a following because statisticians, engineers and scientists without computer programming skills find it easy to use. R is really important to the point that it’s hard to overvalue it and also allows statisticians to do very intricate and complicated analyses without knowing the blood and guts of computing systems. Therefore, my vote goes to R.