FRM alone would not be effective unless we know how to use the computational power of software or applications.
FRM will be more effective if we have some domain knowledge in technology because banks today use lot of software to assist in decision making. As FRM is quantitative in nature it is impossible to have confidence on numbers unless we use some advance software.
Anyone would like to opine and help the aspirants?
Very good topic to adress! I have exactly the same thoughts during studying for the exam (“how will I finally apply all these knowledge in my job without basic knowledge of programs like R or so?”)
When it comes to simulations and portfolio risk modelling, which can take huge amounts of computing power/memory, I would advise learning Matlab. SAS is used for statistics in instiutions and can be used for Portfolio modelling, but its very clunky. Note: SAS is an industry leader. It really depends on the area of risk you work in. Risk Modelling, Model Risk, Risk Classification tend to use Matlab/Python/R.
If you know how to use python and optimize its speed, Python is great for FRM type work.
R is built for statistics and a great first learner but it is a lot slower than python. Python in general, including statistics. can do a lot more.
Learning a low level language is very important too, such as C or C++. If you learn basic C Code, you can speed up Python tremendously but also have the statistical, datamining, web interfacing, general purpose work horse of Python. This is where Matlab is weak, but it is a beast at simulations and matrix operations.
To summarize, You should learn SAS for industry, Python for General Purpose/Statistics and R, and one low level language. Also learn SQL to interface from data warehouses! See coursera for learning R, SQL Python. Edx for Python and C.