Gina Trapani, former editor of tech blog Lifehacker, is quoted in the article:
“Learning to code demystifies tech in a way that empowers and enlightens. When you start coding you realize that every digital tool you have ever used involved lines of code just like the ones you're writing, and that if you want to make an existing app better, you can do just that with the same foreach and if-then statements every coder has ever used.”Farhad makes the point that programming is important even in traditionally non-computational fields: if you were a travel agent in the 90's and knew how to code, not only would you have been able to see the approaching inevitable collapse of your profession, but perhaps you would have been able to get in early on the dot-com travel industry boom.
Q&A sites for biologists are littered with questions from researchers asking for non-technical, code-free ways of doing a particular analysis. Your friendly bioinformatics or computational biology neighbor can often point to a resource or design a solution that can get you 90% of the way, but usually won't grok the biological problem as truly as you do. By learning even the smallest bit of programming, you can at least be equipped with the knowledge of what is programmatically possible, and collaborations with your bioinformatician can be more fruitful. As every field of biological research becomes more computational in nature, learning how to code is becoming more important than ever.
Where to start
Getting started really isn't that difficult. Grab a good text editor like Notepad++ for windows, TextMate or Macvim for Mac, or vim for Linux/Unix. What language should you start with? This can be a subject of intense debate, but in reality, it doesn't matter - just pick something that's relevant to what you're doing. If you know Perl or Java, you can pick up the basics of Ruby or C++ in a weekend. I started with Perl (using the Llama book), but for scientific computing and basic scripting/automation, I would recommend learning Python instead. While Perl lets you get away with sloppy coding, terse shortcuts, with the motto of "there's more than one way to do it," Python forces you to keep your code tidy, and has a model that there's probably one best way to do something, and that's the way you should use. Python has a huge following in the scientific community - chances are you'll find plenty of useful functionality in the BioPython and SciPy modules. I learned Python in an afternoon through watching videos and doing exercises in Google's Python Class, and the free book Dive Into Python is a great reference. If you're on Windows, you can get Python from ActiveState; if you're on Mac or Linux, you already have Python.
Slate - You Need to Learn How to Program