Dataviz MOOC Review: Brace yourself, enjoy the ride

That's it, the MOOC on infographics and data visualization by Alberto Cairo is done, over, finished, completed. Phew. It was more work than I expected. It says four to six hours per week, but for me it was more around ten to twelve; less at the beginning and more towards the end. It was not easy to manage having a job and a family, like many other students and the teacher himself. Several students posted their work mentioning their difficulties in meeting deadlines and class requirements. That's why six weeks were really enough and, after going to bed at 4 am to finish my final assignment (the good old days), I am still recuperating. Still, I was stimulated and motivated enough to make it to the finish line. I hope we'll know how many of the 2000 made it.

Several people have already provided their review (here, here, Knight Center, and Cairo himself). Here's mine.

The good

The engagement of the teacher. It was impressive to see him everywhere making comments on a majority of discussions and projects, at least in the first few weeks. It was also motivating because it gave credence to the discussion forums. I was impressed by his engagement on Twitter and his generosity in shining a light on the students' blogs and entries. In fact, it motivated me to start this blog.

The Functional Art is a great book that will complement those of Stephen Few and Edward Tufte. I especially like the interviews at the end to give a perspective on the industry.

The journalistic angle. I had never seen this in my other readings, all of which were focused on more technical aspects of data visualization. The insistence on finding and showing a narrative has changed the way I do data visualization for the best.

The exercises. I'm glad we were pushed to produce something within relatively short deadlines. It is one thing to criticize, but quite another to deliver. I leave the class with something to show and that's something.

The network of students. It was one of my objectives when joining this class to find fellow data visualizers and this goal has been achieved in large part thanks to the forums and the regular interactions. I hope to stay in touch and continue to follow a few of the students.

The duration. Six weeks was good - long enough to get immersed, but short enough that it didn't become too much of a burden.

The price. It's amazing that we can get such quality education for free. Where's the catch?

The bad

The limited feedback by participants. It had greatly improved in the last week, but earlier I've seen a lot of feedback limited to some "good job, I like your colors". It sometimes looked like someone's just trying to tick a box for class participation. The initial 500 words request that was waived might have been to high, but there should be a threshold. We can benefit a lot from the feedback of our fellow students, but also from a careful consideration of their work. In fact, this experience convinced me further than team work can improve this kind of output.

The ugly

The platform. A few years ago, it would have been good enough, but nowadays, we are used to much more response and intuitive user interfaces and it is hard to adjust one's expectation to a rather antiquated one. I could barely find my way around in the first week and it never got comfortable. There were features so deep that they might as well be hidden. For instance, who else realized that we could establish "contacts" with other students, à la Facebook? Even I had a hard time remembering where my list was (it's under Messages). The inbox format is most bizarre, with messages taking a sixth of the screen width. I also wish that the images could be seen in line, even if hosted elsewhere. I am not sure it is worth trying to fix this platform and suspect it would be better moving to an entirely new one.

I have much more high than low points. My experience was very positive, both for a MOOC and for the knowledge. I would do it again in a heartbeat and in fact, I'll now be on the lookout for MOOCs and other data visualization training.

tl;dr: take it, brace yourself.

MOOC Weeks 5-6: UK Aid to India

For our last assignment of the MOOC, Alberto Cairo decided to give us enough rope to hang ourselves: "do whatever you want". I proceeded to swiftly spend half the allocated time deciding on a topic. Returning to aid, the subject of week 3, was a natural fit and I knew the data would be available. After considering a few generic variations on the themes "where does aid come from" and "where does aid go", I realized I needed an angle. The recent announcement by the UK that they are cutting their aid to India seemed intriguing enough and calling for some data. Then, I set as my goal to create one of these long, vertical infographic, but without resorting to some of the misleading and unhelpful techniques that plagues too may of them. Let's recap some of the lessons of the first four weeks.

  1. Look for a story in the data.
  2. Convey a narrative.
  3. Use good copy to draw the reader in.
  4. Combine several graphs.
  5. Present the same data in different ways.
  6. Use the appropriate graph for the data.
  7. Pick the color scheme carefully.
  8. Label and include legends.

Here is the result.

UK Aid to India. Francis Gagnon

UK Aid to India. Francis Gagnon

The story is that it is a big deal that the UK will cut its aid to India and there are many ways to understand the causes and consequences. It is a delicate topic and I did not want to turn the infographic into an editorial. It is rather designed to help the reader think about the issue and maybe open a few new perspectives, especially since some of the actors have strong opinions about this shift.

It starts by showing the reader how important this decision is: India is a top recipient of UK aid. Then it goes into a comparison of the two countries, to reflect on their relative economic health. This leads into an exploration of poverty in India and finally an overture towards the other potential beneficiaries of this change, showing this policy decision into a larger context. The sources are also an important aspect of an infographic and I wanted to provide them in a clear way to support the credibility of the data above.

This has taken much longer than anticipated. Dataviz nerds, look for a making-of in the coming days.

Week 3: Aid Transparency

The third week's assignment was right up my alley: aid transparency. It is even more disappointing then that I was not able to complete something worthy. The source data comes from the Transparency Index of Publish What You Fund and takes the form of a ranking of aid agencies according to their transparency score. I expected the students to visualize this ranking, making more apparent the comparison between agencies, highlighting their strengths and weaknesses. I decided to try something different by visualizing the indicators themselves. I thought that it could be a nice way of explaining data transparency by detailing how it is measured. Here is my entry.

Aid Transparency Graph FG

The assignment was for an interactive visualization, so the image below shows some of the interactivity that could be prompted by the users, namely a series of definitions and the capacity to select a subset of indicators.

Aid Transparency Graph FG2

Using Adobe InDesign, means drawing every data point and this took way longer than it should, mainly because it does not add so much to the graph to have very precise data. Most people just give a quick look and are mostly interested in the ways in which the data is visualized, more than the result of the visualization. This point was driven home by the multiple hand drawn sketches of fellow students that did not approach accuracy, but that sometimes conveyed clearly enough their concept. The next week, I wouldn't be caught.

Given the call for a narrative, I spent some time finding and writing some analysis. Again, this is not something that I expect anyone to read -- at least, no one has ever commented on the text -- so it does not seem like a good investment of time.

Aid Transparency Graph FG3

In general, I like to use colors to visually group things, but more than once my audience has been more interested to see things grouped by subcategories, so that's what I have done here with the three categories of transparency. See how the colors are grouped. I have to say that it worked better than I had anticipated.

Aid Transparency Graph FG32

This slide shows only the improvement of each indicator. The main message is that all indicators have improved over the last year, although some much more than others.

In the end, I did not get to produce something of the quality I was hoping for. I picked my colors at random, I did not include a legend, I did not push the analysis, etc. But the week was over and another assignment was waiting. The point is not so much to create a perfect infographic, but to learn and this goal was already achieved.