Parametric vs. Nonparametric I did perform a search for this question (I thought it was bound to be asked), but I haven't found one; hopefully this won't be a duplicate.
I'm trying to decide if I should take a course in stats that is more parametric or one that is nonparametric. 
From a maths perspective, what would be the advantages of learning one over the other? Would one be more advantageous over the other as far as working in industry?
 A: Parametric does NOT mean "Bayesian based".
Here is one definition of "parametric statistics"

Parametric statistics is a branch of statistics that assumes data come
  from a type of probability distribution and makes inferences about the parameters of the distribution

(From Wikipedia).
As Wikipedia goes on to note, most of the common, elementary statistics are parametric. For example, ordinary least squares regression is parametric. Loess regression is nonparametric.
Parametric statistics are usually easier to interpret and may be more powerful (in a statistical sense) but they are based on more assumptions than nonparametric statistics. They vary in their degree of robustness, but are usually less robust than nonparametric statistics.
For example, the equation derived from ordinary least squares regression is (in most cases, anyway) quite easy to understand. That from a regression involving splines is often much less clear and may require graphical representation to be understood well. 
Bayesian statistics is something altogether different, having to do with using prior information. 
