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What Knowledge Gaps Do Self-Taught Programmers Generally Have?
BeardedChimp writes "I, like many others here, have learned to program by myself. Starting at a young age and learning through fiddling I have taught myself C++, Java, python, PHP, etc., but what I want to know is what I haven't learned that is important when taught in a traditional computer science curriculum. I have a degree in physics, so I'm not averse to math. What books, websites, or resources would you recommend to fill in the gaps?"
Being self taught myself, I think the biggest downside is some of the strategies and standards that are taught in the mainstream curriculum (IE: how to properly use the object-oriented model, etc.). Especially when I first started out, my code may get the job done, but it wasn't the cleanest or best approach. Luckily, I think it will come to you in time if you focus on improving your code.
Design Patterns: common "Template" solutions to regularly encountered problems/variations-on-that problem. Be careful when learning these that you don't fall victim to "when you have a hammer, everything is a nail". Also learn the Anti-patterns, wikipedia has a good list of anti-patterns.
Algorithms & Data Structures: Analysis, average running time Big O is most important, but understanding worst-case runtime is important too. Designing algorithms vs knowing when to leverage an existing one.
the C++ standard library provides a great many of these, it has a high efficiency sort (from ), it has good collection data structures (vectors, linked lists, maps, etc)
Objected Oriented Analysis And Design: Knowing when to make something an object, when and how to use inheritance and polymorphism, when to not make something an object. Plain old data objects. separation of responsibility: UI is not logic, logic is not UI.
Threading: proper thread synchronization techniques (mutexs, semaphores, conditions, etc), threading patterns such as Producer-Consumer, Inter-process communication
Automata & Computability: (Deterministic|Nondeterministic) Finite State Machines, Regular Languages, Turing Machines
Programming Languages: LL language parsing & rules authoring.
Computer Architecture: Processor design, pipelining, caching, function calling conventions, etc - how to use this knowledge to write more efficient programs
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Seconded. As a self taught programmer, classic algorithms are always my weakest link when I do an interview. Unfortunately, it's also the most glaring, since they usually teach that kind of stuff early on in COMP SCI. I had an interview with Amazon a while back and in preparation bought the book "Programming Interviews Exposed". It's a pretty good resource for getting your hands dirty with all the algorithms that a general programmer should know.