I’ve long been an advocate for statisticians (and other data scientists) to become proficient in multiple programming languages. Fifteen years ago, I said, “Think CPR: C for speed, R for interactive data analysis and graphics, and Perl for manipulating data files.” I’d now substitute C++ for C, and Python or Ruby for Perl, but the general sentiment stands: use the language that best fits the problem at hand.
More blather about languages
Long ago, I wrote a tutorial on Perl. I’d intended to also write tutorials on C and R, but never got around to them and just wrote resources pages (see Intro to C and Intro to R).
My Perl code was always terrible: “read-only”, one-off scripts to convert data files from one format to another. I much prefer Ruby, but my Ruby coding involves a whole lot of googling. Python is more popular, and so I’d recommend that to you. (But I really don’t like the handling of regular expression in Python, and that’s like the main thing I want from that sort of language, so I’m sticking with Ruby for data file manipulation.) And these days, what with the whole tidyverse developments, you can probably get by with R alone, for data file manipulation.
Still, sometimes a problem requires you to bring out the big guns. For lots of problems, my most pristine R code won’t be nearly as fast as my sloppiest C or C++ code. So to be fully self-sufficient, I think you want to be able to switch to a lower-level, super efficient (for calculations, not for writing the code) language. Many would advocate for Julia as a way to have one language that is simultaneously easy, readable, and fast. But I still believe in switching between languages (C++ for speed and R for interactive data analysis and graphics, and maybe data file manipulation, though I still prefer to switch again to Ruby).
Pjotr Prins had me writing D for a while,
and he continues to advocate for it. And boy was I ever glad to switch
from C to C++ and from .C() to Rcpp. html
and CSS go without saying. And we should probably add JavaScript, or
something that compiles to JavaScript. (I like
CoffeeScript; in fact, CoffeeScript is
probably my favorite language, presently.)
There was also SQL and MongoDB in there, for me. Data scientists need to be comfortable with accessing formal databases.
I’m now trying to learn PureScript, but I’m finding it heavy going and so am taking a detour into Haskell for a bit, to try to get the basics of the “purely functional programming” business. But I intend to then go back to PureScript.
How to learn programming language
Anyway, that was a bit too much of a blathery introduction to say: I think it’s good to learn multiple programming languages, and in having tried to do so myself, I’ve got some suggestions about how to go about it (the learning), which might be useful.
- Set aside substantial and regular blocks of time.
- Buy a good book. Or buy all the books.
- Have a particular goal/application in mind.
- Develop a script, with notes, illustrating key language features you’ve learned.
- Force yourself to use your new skills.
Set aside substantial and regular blocks of time.
Learning a new programming language takes time and regular practice. And you really need blocks of 2-4 hours, because if you’ve not touched the language in a week, it’ll take you 30 minutes to get yourself back into it. (What were you doing? What had you learned? What did you want to look at next?) You need a chunk of a couple of hours so you can get yourself back into it and then make a bit of progress.
Buy a book. Or buy all the books.
I recommend finding a good book and following along. Actually, I tend to buy all the books. But you want at least one, maybe two. Which book is a matter of taste. I’m looking for something that is aimed at someone who can already program in something. But whether a book is the right one for me depends mostly on the types of examples that are used. After that is writing style and quality of explanations.
For example, regarding Ruby, many people recommend Programming Ruby, but personally I found the examples and the order of topics was just not a good fit for me. I much preferred Eloquent Ruby and then The Well-Grounded Rubyist.
I’ll usually start by looking for something published by O’Reilly; they’re uniformly good books. But in reality my favorite books are published by others: The best book (for me) on a given language is likely not from O’Reilly.
Have a particular goal/application in mind.
If you really want to make progress in learning a programming language, you need to move beyond the examples and exercises in a book and start using the language creatively to solve your own problems. Perhaps you have a particularly wonky data file that you want to parse and reorganize. Or maybe you want to write a web app that serves up local bus schedules. I like to switch back-and-forth between slowly developing an understanding of the language and its features, and then hacking away at something more substantial and real.
Develop a script, with notes.
My key piece of advice: as you’re learning, develop a bit of a script with comments and other notes that illustrates the various language features that you’ve learned.
First, you want to document the details of setting up the various language tools (compiler/interpreter and libraries and such) and how to keep them up-to-date. (This stuff is tedious, painful, and critical. So once you’ve figured it out, you’ll want a record of it so you don’t have to go through it again.) You can find various of my notes on this stuff in my ProgrammingNotes repository on GitHub.
But most importantly, you want an informal script that you can quickly skim through or search, because you’ll forget stuff:
- How do define a function in this language?
- What do I use to indicate comments?
- How do I concatenate two vectors?
- How do I search for an element in a vector?
- What was the syntax for list comprehensions again?
It’s all in that book that you bought, but it’s much easier to find that one thing you’re looking for in the succinct set of notes that you wrote yourself. Here are some examples of what I’ve done: for Python, Ruby, and JavaScript/CoffeeScript.
Force yourself to use your new skills.
Finally, if you really want to adopt the new programming language as part of your working toolkit, you’ll need to force yourself to actually use it in real life.
There’ll come a time when you’ll say, “I should do this in Ruby. Nah, I could get it done in Perl in 15 minutes and it would take me two hours to do this in Ruby.” But you’ve got to force yourself to use the new language. In the short term, it’ll be easiest to switch back to the language you’re comfortable with, but if you want to make progress in the new language, you’ve got to put it into regular practice, and that won’t be easy. But it’ll get easier.
Ultimately, you’ll have four or five languages floating around in your head and you’ll not remember how to define a function in any of them. And then you’ll be really cool.