Can a MOOC Boost Your Career? This One Can!

Published: Jun 04, 2013

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When it comes to career advice, here at Vault we've always been of the belief that the best people to dispense it are those that have lived it. Thus: when we release our rankings of law, finance, accounting and consulting firms, all of the opinions in the firm profiles belong to verified, practicing employees of those firms—something that we spend a lot of time and energy to get.

In that spirit, earlier this year I decided to try out one of the tools that we've recommended here for career advancement—MOOCs, or Massive Open Online Courses. For those who are unfamiliar, MOOCs are courses commonly offered for free by a variety of sources (sometimes for-profit companies, sometimes universities, sometimes a collaboration between the two). Often serving as introductions to key concepts, some use the courses as an easy way to get a feel for a new subject area before deciding to make a full commitment to a program; other enjoy learning a broad range of subjects on their own time.


Focusing on skills

My own question coming into a MOOC program was whether one could, by itself, prepare someone to take a step forward in their career—is it possible that one semester-long course could lead to a change of direction, or give someone the edge they need to get ahead in a current role or in the hiring market?

For that reason, I focused on attempting to acquire a skill set that is both in ever-increasing demand in the wider economy, but also of specific interest to me in my career in the online world: computer programming. 

As someone who is both old enough to remember the world before the internet and young enough to have the bulk of my career ahead of me (that's code for mid-30's, folks), having the ability to at least understand what programmers do seemed like a fairly solid goal at the outset. And who knows—if it turned out that I was any good at it, maybe I could envision a switch from providing the content that populates websites to being the guy who writes the code for them instead.


Choosing a course 

My first step was to select an appropriate course. While there are a number of introductions to programming out there, and a variety of languages to choose from, I opted for the course on offer from EdX—MIT's Introduction to Computer Science and Programming. There were a number of reasons I chose that course: first, the course covers not only a language—Python—but uses it to introduce key computational concepts that are applicable across a variety of languages. Additionally, I will admit to being swayed not only by the prestige of the institution, but also by the certificate on offer to those of us who completed the course.


Getting started

 One of the things I liked most about the course was that, while it was offered for free, at no point did it feel like any aspect of it had been neglected or put together as an afterthought. Video lectures were presented by full MIT professors, the school provided a textbook specially written for the course (it was available for purchase, but a free online copy was also made available to students), and there always seemed to be plenty of MIT grad students or adjuncts hanging around in the course's forums to help students who needed it.

What I had not expected from the course was the amount of time it took to complete. As someone who has plenty of time to kill on public transit every day, I had planned to take care of the bulk of the work during my ten hours spent commuting every week. As it turned out, that figure represented only around half of the amount I time I put in on a typical week. Indeed, with a variety of assignments to hand in each week, the course regularly kept me up well past my usual bedtime, and ate into a good chunk of my weekends on occasion too. However, that same pace of work ensured that my interest never flagged—there was too much to learn and figure out to get bored, while the student discussion forums proved an invaluable source of both solace and advice when the going got really tough.


What I learned

Looking back over the course, I'm amazed at how much I've learned about programming and computational thinking in just 15 weeks. In that time, I've gone from not knowing anything about programming—apologies to my high school IT teacher if you're reading this!—to feeling confident that I could use Python to tackle some fairly complex problems.  As an example, while the opening weeks of the course had students writing programs that effectively mimicked things you can do quite easily in Excel—calculate compound interest rates and the like—by the later stages we were being asked to write code capable of modeling fluctuations in disease populations, while taking a number of different factors into account.


A new career? 

For as much as I've learned on the course, there's no way that I'd be equipped at this point to do any kind of work as a developer or programmer. That's not to say that it would be true of everyone—the knowledge and ability displayed by some of my fellow students (and there were thousands of us, from all over the world) in the discussion forums clearly surpassed my own, and leads me to believe that many of them would indeed be equipped to take jobs straight out of the course.

But the knowledge I have taken from the course has already helped me to understand the solution to an issue that we had been dealing with here at Vault.* That alone has almost made the time and effort worthwhile, not to mention that I now have a new interest that—while I may never pursue it as a full career—at least leaves me better equipped for further study, and to better understand the environment in which I make my living.

In conclusion, while I can't speak for all the MOOC courses out there, I can whole-heartedly recommend this specific one for anyone who would like to know more about a skill that readwrite this week called "the core skill of the 21st century." As for those other courses—I'm currently eyeing up my options for courses that start after I've had a suitable (and well-earned) break!


* It came down to computational complexity—in MIT-speak, we reduced the 'Big O' complexity of an operation from O(2N) to O(N).