While I find the first 2 in (a) to be quite interesting--I have a math degree--you'll benefit from real analysis if you want to go on in mathematical statistics because you'll have to study measure theory. This typically coincides with studying more analysis (beyond real valued spaces) and metrics defined on those spaces (which is what probability *is*).

When it comes to (b), you can benefit greatly from learning numerical methods, if you plan to go on and want to do computational data analysis. That's sort of the route my applied skills have developed, because a lot of my analysis revolves more around how to manipulate and analyze data through programming. While I'm not sitting here writing integration functions, depending on how they teach it, the matrix techniques and monte carlo simulations can be useful. With that said, vector algebra is typically studied in stats, for the obvious reasons that a lot of stat formulas are expressed in the language of matrices. Differential equations hasn't always been too significant, but I'm finding in certain areas (time series, fourier analysis), it has its place. It's really the one thing that's kept me from studying signal processing.

If I had to choose, I'd probably go with (a) if I wanted to be more theoretical and (b) if I wanted to be more applied. I love pure mathematics, so I'd have chosen (a), which is sort of the boat I'm in now, but for work, most of my skills are derived from (b). Honestly, though, the best education has been experience for me, and that's been through work. Therefore, I find it a good choice, in my case, that my education has been largely theoretical. I have a solid foundation from which I can build upon in applied settings.