I suppose it really depends on what you want to do and who you want to be doing it with... and what capabilities you want at your own core and what capabilities you can hire, borrow, etc.
In most situations you go into, you're going to be part of a team, and that team will probably have specialists... people who develop database systems, people who develop front-end web applications/tablet-apps, people who interface with the business to get requirements and feedback, people who run ops and keep a system running 24-7, and people who are experts at the analytic methods that are being used. So as you look to that kind of set up, where do you want to be?
It seems to me, the real nut of "data science" is understanding the analytic methods; what they can and can't do, what data they need, what you can expect from them, what happens when they fail, and how they can mislead you. Everything else can be hired: python programmer, r programmer, database developer, etc. And if you're doing anything bigger than a small project, you probably want people who are experts in each of those.
That said, to get good at data science, you're probably going to have to get really good at one system like R, Python, SAS, etc. where you can apply and refine your knowledge of those analytic methods. If you only have so much limited attention span and ability to maintain expertise (e.g. "use it or lose it"), then you probably want to focus on using just one or two tools in learning those methods.
It's easier to hire a top data scientist and an expert in the ___ programming language than it is to hire a single data scientist that is also an expert in the ___ programing language.
That said, it's probably worthwhile to look at the top languages and learn how to do some basic things in those so that you don't completely exclude yourself from potential jobs, but I think it's also easier to be a master of the methods and learn a new language as you have to - or team up with that language expert - than it is to take an expert in some language and teach them the nuances of the analytic methods.
Look at what you want to be doing then focus on the hardest thing there is to do in that area - that's what will allow you to stand out.