This post is slightly modified from an email that I wrote to some friends who were looking for tips on getting started with scientific computing with MATLAB. I'm not the world's expert on the subject, and I traded MATLAB for Python a couple years back, but the advice should be solid for newcomers in either language.
There are 3 broad categories of matlab/general coding best practices: performance things, code reusability, and readability.
Preallocate arrays (make empty ones of the final size) instead of creating them within loops; otherwise MATLAB will have to find new space in memory for the new array, make it, and delete the old one, etc. each loop iteration.
Learn to vectorize where possible instead of for loops.
Other, more general tips:
The basic, or at least most important, principle of this is what is called 'abstraction', or breaking complicated procedures down into smaller, generic pieces. Writing functions is the classic example of this. Where possible, functions should be short (50 lines max) and general enough that they can be used for many things. More technically, abstraction refers to making code more modular, sort of like Henry Ford's 'interchangeable parts'. Your time series analysis functions should be able to take arbitrary types of data, with arbitrary time steps. It should be easy to adjust the time window for a rolling mean, or change from a boxcar to a gaussian smoothing, or use median and median absolute deviation instead of mean and standard deviation, without having to rewrite a lot of code. This doesn't mean that you have to write super generalized libraries, but if functions are well-designed and abstracted, even if you have to write a different one, you can often mimick the design of the function, and make few or no changes to the input arguments or its outputs (the design of input and output, etc. is often referred to as the 'API' or 'application programming interface', especially for larger libraries or programs).
A big corollary of this is "DRY" coding (Don't Repeat Yourself), so that a function or workflow is only written once. Therefore, it always does the same things, so all calculations will be consistent. Then, if you need to change the way it is done, it will change for all scripts and all variables that use it. You will also write much less code this way, making yourself more efficient.
This will also make it much easier to debug code and to have others (collaborators, reviewers, students, yourself in one year) figure out what the code does and how to use it.
Style and readability:
Remember that computer code is to be read by humans and only incidentally by computers.
The biggest things in this are to insert spaces in between variable names, operators (+,-), etc. Put blank lines in between lines of code (where appropriate) and don't put more than 2 blank lines in between anything. Most importantly, make function and variable names long and descriptive. Matlab programmers are particularly bad about this. It's cultural and it's horrible.
and this one is really good in general, not specifically about matlab (and written by economists):
Matlab programmers often make variable or function names look like "bsxfun" or "BSXFUN", which means... who the hell knows. I think it stands for 'basis function' or 'basics function' but I don't know. Therefore if I see it, I'm confused, or if i am trying to look for a function that does whatever it does, I can't find it easily. C/C++ programmers will write 'basisFunction' in what is called 'camelCase' by others, and many think this is ugly and somewhat unreadable, but is certainly better. The convention used by everyone else (except Mathematica programmers, where this is not allowed) is 'basis_function' which is nice and readable.
Also, make all your code less than 81 characters wide. Split your lines. This makes things much easier when you have several scripts, or an editor window, a terminal, and a journal article, all open on the same screen.
Probably the best way to accomplish many of these goals is to use Python instead of Matlab... It's much easier to write fast code (though the language itself is about as fast) because code is more often automatically vectorized, the syntax is more clear, and it's much, much easier to write functions and to organize them. The users are often more computer literate, so more experienced help is easily available. Plus it's free and open-source, so you can actually see what the code does, and install it on any computer for free. In fact, every modern computer already has it, minus scientific libraries. IPython Notebook is also the best thing in the world for prototyping scientific code and doing any data analysis. Pandas is incredibly useful, as is JobLib Parallel. Did I mention that it's free?
There are three main reasons to use version control. USING IT IS REALLY EASY, TOO. Start doing this the absolute next time you do any coding. You will definitely not regret it. http://hginit.com/ <-- read this tutorial/ introduction. It's really fast and kind of funny, even if you don't end up using Mercurial.
The first is that it saves versions of the code, so you can go back at any point and see changes, or revert to a previous, working version if you screw something up. The second is that you can easily back up and share code at GitHub or BitBucket, in a way that is easier to use and much less screwupable than Dropbox, because the entire code history is automatically saved as well, and it's not automatically synced to your computer, so if you delete it accidentally, it's still online.
The third is that you can have several concurrent versions of the code and switch back and forth between them, and merge them when need be. Also, other people (collaborators, students, anyone...) can also 'fork' your code and make their own changes, and if they want to add something, they can. And you can keep it all synced. I do this between computers because I typically work alone, but I share data with the world via GitHub and one day hopefully I will collaborate with people.
There are a couple different types of version control, centralized version control (SVN is the most common) and distributed version control (Git and Mercurial (also called Hg, the chem. symbol for mercury) ) are the most common. You want distributed version control.
Git and mercurial both used to be used about equally, but git has pulled ahead in recent years, because of 'cooler kids' using it, because GitHub (which is awesome) uses it exclusively (vs. BitBucket), and because it's a bit more powerful. The first two are basically network effects. Mercurial is a bit easier to use off the bat, I think, but both are pretty similar in basic usage. Both git and mercurial have good GUIs now, too (SourceTree is the best I've used), which makes learning a lot easier.
I used to use mercurial but have switched to git, because I like github so much, and because more people use it commercially, so I realized that if I want a job doing anything with software, or want to contribute to open-source projects, I will have to know it.
GitHub has free public code repos (repositories) but you have to pay for private ones. BitBucket isn't as great a website/code host, but has unlimited free private code repos for academics.