I feel my education in CS was pretty good, but then again I started before the dot-com-boom (although only by a few years). I did not learn how to expertly program in any particular language, but was taught the paradigms, tools, and capabilities of many languages: Pascal and C for modular and system programming, Lisp for functional programming, MIPS RISC Assembler for low-level programming (though we only “ran” our programs on a MIPS emulator for Windows that greatly over-simplified I/O – but the point wasn’t to learn I/O in assembly but to be familiar with assembly, how it is an analog to the way modern processors function, and how it was a precursor to higher-level languages), and Smalltalk for object-oriented programming. I then had a junior-level class that was a survey of programming languages and paradigms which projects in C++ and Prolog. The focus wasn’t on learning everything about a particular language, but learning concepts – like OOA/OOD, functional programming, decomposition and abstraction, and (everyone’s favorite) pointers.

Prolog, the declarative language that relies on a predicate calculus resolution engine, was the coolest – but also seemed the least practical due to the level of abstraction in which one had to work in order to accomplish even simple concepts – like data structures. It is cool because it is different and makes for great problem-solving exercises. Despite its impracticality for most problem domains, it is absolutely essential for others.

I do agree that newer graduates and students – since the dot-com-boom – seem to be taught some of these concepts more superficially. And, as Melinda also observed, it was scary how many students had great difficulty really grasping some of the programming concepts but were still able to pass.

I’m fairly certain that math and science are still at the core of Georgia Tech’s program – which required a decent bit of physics, calculus, and lab science of all science and engineering students. And when I was there I had to take some painful algorithm classes, too (I can think of at least one sophomore class and one junior class). But that isn’t enough for CS. Skills needed by serious software engineers (vs. code monkeys) include a much greater ability to think abstractly. I think this is where learning lambda and predicate calculus really pay off. This level of abstraction requires a different way of looking at problems – something that can’t simply be learned while learning match and science or while learning how to program in a particular language.

It wasn’t until the 1990s that the demand for developers became so great that just about anyone who could read an O’Reilly book could get a job writing code. CS programs began advertising themselves (quite of their own accord) as the hottest ticket on campus — the best way to get a great job fast. So they attracted an entirely different sort of student (the sort with no background in actual programming). Now, CS programs are seen a lot like accounting programs: just dull job training.

And there’s something good about this. Because the world needs a lot of code written. So, if standard libraries help average people write code that gets the job done, so be it. Would you really expect the average construction worker to have a degree in architecture? How many houses would get built if that were the case?

So, in defense of code monkeys, there’s a lot of code to be written, and the logicians sure ain’t gonna do it.

As an IT Consultant, I have begun to realize how important it is to focus away from a specific language and focus more on the basic concepts, commonly used algorithms, and best practices. Anyone can pick up a book and learn to program in a language, but without a foundation that crosses the programming language barrier, you’re just a code monkey.

This degree business is a bit of a touchy subject with me since I don’t have a CS degree, and I haven’t decided if it makes sense to go back and get one or not. So this was an interesting post.