There is a question with similar intent on programmers.SE. That question has some quite good answers, but the general theme seems to be that without self study, you get no-where.
Obviously there are some major difference between programming and statistics - with programming, you're really just learning some basic logic, and then applying it repeatedly. New languages all use the same basic concepts. Self study allows you learn more advanced concepts, and become more efficient. This kind of stuff is quite difficult to teach.
Statistics is quite different. It's easy to apply the logic involved - because somone else has usually laid out the methodology. Indeed, the methodology is usually most of what is taught in universities. But statistics is really far deeper than that, and involves some really high-level concepts. It's hard to even look for those concepts, if all you've be taught is applied statistics, let alone to understand them (although I wonder how much this may be due to jargon in the field). Also, I find that self study in programming involves reading a lot of short articles/blogs to introduce yourself to new concepts, whereas accessible articles about stats are nearly always aimed at the total beginner, and are therefore somewhat useless to an advancing novice, like myself.
So the question is: Is self study more or less appropriate than a university education, for statistics? What methodologies for self study are there that work? Any examples of what has worked for people before would be welcome.
(this probably should be a community wiki, but I see no checkbox)