I absolutely agree that knowing basic CS is a good thing, and it will make you a better developer. Also, we do internalize a lot of the concepts so they become second nature. That being said, it surprised me how much code there is where this doesn't matter.
As for the specific questions: It's definitely good to know big O notation, so maybe I was a bit quick to dismiss the first question. However, a more realistic/useful comparison would be between O(n^2) and O(n log n).
For picking a collection to use – definitely knowing when to use a hash map versus a list. Asymptotic run-time behavior of Quicksort – no.
Binary search tree – that's the one example I mentioned in my blog post, so yes, useful.
Graphs – never needed in the applications I've worked with. Same for using a heap, never seen/needed.
Graphs are an interesting case in this debate. I agree that you very rarely run into real-world problems that look like a traveling salesman or bipartite test etc. But the thing about graphs is that they are the foundation of ALL data structures, and lots of algorithms. Lists, hashing, trees, arrays and more -- just special cases of graphs. If you can describe anything at all with a boxes-and-arrows diagram, it's a graph problem ;-) so if you know at least the core concepts of graphs, including some fundamental techniques (e.g. advantages of each representation such as adjacency matrix vs. node list vs. edge list), then you have the tools to work through any data structure problem; and any algorithm that's focused on searching or manipulation of specific data structures. And this can take you very, very far.
Once again, of course you can learn most of these foundational concepts more empirically, or in a more "bottom-up" way by starting with the standard data structures and algorithms that you use in practice, but gradually deducing general concepts that unify them. But I wonder if this can ever result in the same deep insight that some study of graph theory can give.
As for the specific questions: It's definitely good to know big O notation, so maybe I was a bit quick to dismiss the first question. However, a more realistic/useful comparison would be between O(n^2) and O(n log n).
For picking a collection to use – definitely knowing when to use a hash map versus a list. Asymptotic run-time behavior of Quicksort – no. Binary search tree – that's the one example I mentioned in my blog post, so yes, useful. Graphs – never needed in the applications I've worked with. Same for using a heap, never seen/needed.