There are really only two 'kinds' of statistics (frameworks is probably a better way to put it), Bayesian and frequentist. He's really talking about topics and types of analysis in statistics that most people aren't familiar with. Most people don't learn anything besides completely randomized trials and simple random samples in stats 101, but there are a lot more options for the design of experiments and surveys that might improve your estimates or increase efficiency. You can also get biased estimates if you do stumble into a situation where you fail to account for a study design. What advanced topics you might want to look into depends on what kind of job you'd be interested in. For example, he mentioned time series modeling. If you are interested in being a quant at a big investment firm, that's going to be required but if your going to be working on determining insurance rates for cars that's going to be of limited utility and something like survival analysis would be necessary. For an average person, you can look at this thread and see things that might set you apart from the crowd. Every other post says 'bell curve' or normal, but what happens when normality fails? What about when normality is impossible (categorical data)? Knowing how to analyze data non-parametrically, knowing what the assumptions are for the analysis you are doing and which statistics are robust to violations of those assumptions anyway (and being able to explain that to others) are very important skills people miss out on in introductory stats courses.

As far as statistical resume builders, learn SAS or R (but only after you know the statistics. There's no point in giving out estimates for things if you don't know what's going on under the hood.) You can also learn winBUGS and blow them away with your perceived Bayesian knowledge and probably get away without ever using it. As far as actually learning Bayesian techniques, it's a real wow on the resume but in most cases people expect to see frequentist stuff anyway, so it's of limited utility (and will absolutely obliterate your GPA.) on most jobs.