Learning All the Time

Over the next two months, I am participating in three different Massive Online Open Courses (a/k/a "MOOCs"). It's good to remind myself now and then that the edge of my rut isn't the horizon. It's time for me to make my brain hurt again.

I've taken online courses before, but never with so many classmates---and not in such a low-stakes way. Although I want to make the best effort I can, not paying for the courses (or having them show up on a transcript) gives me a bit of an "out," if I get overwhelmed.

Here is what I've signed up for:

Computing for Data Analysis

This course started on Wednesday and runs until January 30 (and has ~40K people enrolled). Taught by Roger Peng from Johns Hopkins, the description states that "this course is about learning the fundamental computing skills necessary for effective data analysis. You will learn to program in R and to use R for reading data, writing functions, making informative graphs, and applying modern statistical methods." Yikes. I haven't had an ounce of formal programming coursework since high school---you know, back when BASIC and the TRS-III were king. But I hear about R a lot and am curious about how to use it. It's time for me to get on board.

So far, so good. I've completed watching the lectures for week one and have taken a ton of notes. I'm almost finished with the first programming assignment/quiz---just one question left that has me stumped. I know how to solve it with Excel. In fact, the entire assignment would be much easier (for me) in Excel. But I am trying not to "cheat" by doing it in Excel first and then checking my answers in R. I need to know how to program...and the only way to do that is to get my hands dirty with R.

The most important thing I've learned so far is that syntax can be an unforgiving master to serve. Excel will give you some leeway between upper and lower case, for example. But R is exacting for every piece. 

But, hey, I've survived the first week in good stead...and that's 25% of the course. I feel more confident (even if it's a false sense) than I did when I signed up for this. Maybe I really can do this.

Introduction to Infographics and Data Visualization

Some of you may remember posts on other data-minded blogs this fall about this course led by Alberto Cairo. This will be the second offering, starting on Saturday, January 12 and wrapping up on February 23. This time, it's bigger (6K students) and has a few more tools available.

From the syllabus: "This course is an introduction to the basics of the visual representation of data. In this class you will learn how to design successful charts and maps, and how to arrange them to compose cohesive storytelling pieces. We will also discuss ethical issues when designing graphics, and how the principles of Graphic Design and of Interaction Design apply to the visualization of information. The course will have a theoretical component, as we will cover the main rules of the discipline, and also a practical one, as you will learn how to use Adobe Illustrator or Tableau to design basic infographics and mock ups for interactive visualizations."

I'm totally psyched about giving this one a try. I'm excited about learning the basics of Illustrator. I've played around a bit with Tableau before, but this will give me a reason to go back and dive deeper.

Data Analysis

Because the overlap in the first two courses is apparently not enough for me, this course also starts this month (January 22) and then runs for 8 weeks. So, there will only be one week where I will have to juggle all three...and some time when I just have this one to manage (assuming I don't sign up for anything else). This one is a complement to the R programming class I've already started, and is taught by Jeff Leek, who is also from Johns Hopkins.

This course is billed as "an applied statistics course focusing on data analysis. The course will begin with an overview of how to organize, perform, and write-up data analyses. Then we will cover some of the most popular and widely used statistical methods like linear regression, principal components analysis, cross-validation, and p-values. Instead of focusing on mathematical details, the lectures will be designed to help you apply these techniques to real data using the R statistical programming language, interpret the results, and diagnose potential problems in your analysis. You will also have the opportunity to critique and assist your fellow classmates with their data analyses."

I like stats, but it has been awhile since I have flexed those muscles. Not as long as it has been for programming, fortunately. One of the reasons I am interested in this class is the chance to work with bigger data sets, while applying my nascent R skills and reawakening my statistical knowledge. It's a good application of things. 

I'm hoping that I won't be a MOOC dropout. I also know that I've signed up for a heavy dose of learning at a time when I have a ton of travel for work and several projects due. But Opportunity knocked and I've chosen to invite her in to stay for a bit. If she starts making a pest of herself, I'm giving myself the option of evicting her. These courses will be offered again. I can catch her on another round.

Are you taking any (or all) of these courses, too? I have a couple of study buddies lined up for the first two courses, but the more the merrier! What else are you learning this year?