I tried to: analysis of algorithms, perhaps data structures. Analysis of algorithms is the crucial one; if someone writes O(n^2) code when they could be writing O(n), and the constant factor is negligible, and they're unaware of these things, then IMO they have no business writing code that other programmers will use. They might be able to solve their own problems for their own data sizes, but they shouldn't infect the wider world with solutions that break under stress.
For data structures, it's more important to have a taxonomy, and a rough general knowledge of the kinds of data structures that are available, so that you know what's required and when, and have enough confidence to implement any given data structure as needed.
It's also nice to have the time and opportunity to look at different levels of various stacks, such as the network stack (the analog wire signals, how binary signals can be encoded on them, ethernet, IP, TCP and UDP etc.); and things like OS caching, process scheduling, etc. But where you're looking to put those kinds of things into practice, it's not difficult to take a day or two out of your time and read up on them - it doesn't take long to scratch deeper than an undergraduate course covers (IMO).
Depending on what area you target, other things can also be useful. Re compilers and languages, I'd be wary of taking the academic stance too seriously. Classic texts like the Dragon book overemphasize the analysis of finite state machines and pushdown automata, DFAs and NFAs, and the details of grammars and parsing, LL vs LALR, and generally all the data you need if you want to rewrite lex or yacc. But really, parsing is a very small part of writing a compiler and is actually quite easy, unless you're trying to parse C++ or something. And a more modern trend is to overemphasize type systems and their analogies to proof systems; I'd also steer fairly clear of these guys, as they are (IMO) primarily interested in churning out PhDs by creating new mathematical abstractions that have very little practical application.
When downplaying the practical value of much of a CS degree, however, I'm assuming that you already have pretty decent coding chops. For example, I would expect that you wouldn't be intimidated by programming competition problems, for example see some of the problems in the PDFs from this page:
http://acmicpc-live-archive.uva.es/nuevoportal/
The harder problems rely on a relatively small amount of base knowledge, but you have to be pretty comfortable with recursion, generating combinations and permutations, dynamic programming, and often a little computational geometry. The easy ones are more like warmup in how to read and follow instructions.