Certainly in data science, which is what some people call "quant" these days, R and Python are popular. In finance they probably are too, as is Matlab. Certainly in my experience it is 'whatever is most convenient' and if a spreadsheet and VBA will do, then that is what you produce.Going back to the original question, I suspect the OP is being criticised by financial firms on their algorithm design rather than knowledge of a language. I know some investment houses require passing C++ tests, but if several have said the same thing then they probably need to be able to design algorithms easily.Generally I would say to the OP to start off by simulating any process they learnt in mechanics, physics etc. Simulate anything in real life - e.g. if you are at the doctor you might think 'how can I simulate a queuing system that attempts to estimate patient throughput?'. Think about how you would use such a model to make money if, for instance, you bet on the value of the throughput, or another quantity dependent on that etc etc. You will find these examples everywhere in life. Also hackathons would help aswell as you will be put through your paces and cover areas of languages and libraries courses don't. It doesn't all have to be finance related, as long as it gets you to think like a programmer.I remember programming courses in TCD. One course on simulation the lecturer wrote the solutions on the board. That was 40% of the course for successfully typing it in and debugging. And most courses scratch the surface anyway. A classic was when a friend was telling me about his uncle trying a FÁS course in Java, where his cousin kept saying "by the time he starts the course, the content will be out of date". My guess is that the course Daniel offers (if memory serves me he is Cuchullainn) is probably an exception to this and there's gotta be other courses out there that are designed in a way that is actually useful. I also loved Mark Joshi's book on design patterns. You might never use C++, never use the STL library or may never even use any of the solutions in the book as a quant, but his general approach is the business oriented one programmers need. While clever tricks to make your model run in milliseconds are useful for RMs or traders, I find that wannabee quants sometimes forget to also cut down on coding time, and his approach shows how to build code that can easily be adapted to changes in specifications. One final thing, although this is more of a tip to teens/college students beginning is to start off with something like Python, then work on C++, as Cuchulainn says. A few years back I gave a brief course to kids in Scratch. It reminded me of doing VB where by the time I tackled C and C++ my head was well above water so I was only learning to code a new language and OO, not learning algorithms aswell.Scratch is similar - for instance one thing I taught the kids (and which many got) was how the objects in the program can communicate with each other. This is very, very useful when you tackle serious programming languages and work as a quant and, even if just for a bit of laugh it is worth looking at Scratch.
Last edited by liam
on February 19th, 2016, 11:00 pm, edited 1 time in total.