I was recently reflecting at, basic classes aside, I use information from mainly three graduate classes. Two of them were special topics classes, and one was a class that had finally evolved from a special topics class.
In one of the special topics class, we were given a choice of two topics: one field survey of Gaussian processes, which would have been useful but that was not so interesting to the professor, and local time (i.e. the amount of time a continuous process spends in the neighborhood of a point), which was much more specialized (and for which I did not meet the prerequisites) and much more interesting to the professor. I chose the local time because I figured if the professor was excited about it, I would be excited enough to learn what I needed to to understand the class. As a result, I have a much deeper understanding of time series and stochastic processes in general.
The second special topics class seemed to have a very specialized focus, pattern recognition. It covered the abstract Vapnik-Chervonenkis theory in detail, and we discussed rates of convergence, exponential inequalities on probabilities, and other hard-core theory. I could have easily forgotten that class, but the professor was excited about it, and because of it I am having a much easier time understanding data mining methods than I would have otherwise.
The third class, though it was not labeled a special topics class, was a statistical computing class where the professor shared his new research in addition to the basics. There I learned a lot about scatterplot smoothing, Fourier analysis, local polynomial and other nonparametric regression methods that I still use very often.
In each of these cases, I decided to forgo a basic or survey class for a special topics class. Because of the professor's enthusiasm toward the subject in each case, I was willing to go the extra mile and learn whatever prerequisite information I needed to understand the class. In each case as well, that willingness to go the extra mile and fill in the gaps has carried over to over a decade later where I have kept up my interest and am always looking to apply these methods to new cases, when appropriate.
I am currently taking the bootstrapping course at statistics.com and am happy to say that I am experiencing the same thing. (I was introduced to the bootstrap in fact in my computing class mentioned above but we never got beyond the basics due to time.) We are getting the basics and current research, and I'm already able to apply it to problems I have now.
