FAILURE IS IMPORTANT.
I didn’t get into the data school in my first interview, and that’s not uncommon. Failure is a crucial part of life and even more importantly – learning. It’s only natural to be afraid of failing, it would be wonderful if you could do everything right the first time, but the more you fail the less you fail… I think? I dunno, I’m still in the “Failing most of the time” stage.
Any way, so you’re probably wondering how constant failure relates to getting into the Data School? It’s pretty simple – the Data School has a merit-based interview process, you make vizzes, you get told what’s right and wrong, you iterate. Then you do that again. You see if you do vizzes before and during that process (ie FAIL LOADS) you will be better before and during your application. If you are better before your application, you’ll get further through the process – which is your aim? Right?
Quick and Easy Failure!
You need to find a safe environment to fail and then fail over and over again. If are not comfortable with learning through your failures then I personally wouldn’t recommend the Data School. I wouldn’t recommend almost anything if you’re afraid of learning, however. I bet you’re asking how I failed. “Gheorghie, how did you find so many ways to fail that you got to the point where you had some semblance of competence?”. Two ways: Makeover Monday (great way to get in a rhythm of producing vizzes) and showing the vizzes I made to those around me.
Makeover Monday is a weekly re-viz-ening of a data set (and potentially visualisation) run by Andy Kriebel (The Head Coach here at the Data School, so him knowing you from MM is not a bad thing) and Eva Murray (A Tableau Zen Master!). They’re both wonderful and very helpful in their feedback which they do on a live podcast. Most of it is summed up over on their website, but I’ll outline some stuff here that I wish I knew when I started out.
- Either Eva or Andy will post the data almost always on Sunday, this can give you a chance to get in early (as most submissions come Monday onward). You might want to get the data all uploaded on your laptop so you can do your viz during your Monday commute?
- You want to submit sooner rather than later if you want your work reviewed. Depending on the dataset there may be many, many submissions. The earlier you get you viz in, the more likely it will be that your viz is reviewed – they don’t have the time to look at them all.
- You can reviz the viz (get the same message across) or pick a certain aspect of the data. I learnt the lesson of “keep it simple, stupid” whilst doing MM – don’t get to much into the same dashboard.
- Limit how much time you spend on it. I know this might sound odd, but spending more time on a viz might do more harm than good. You can get stuck down what we call at DS a “rabbit hole”, pursing a poor (potentially off topic) idea without any feedback to set you right. The limit commonly given is an hour, but if you’re new 2-3 hours is okay. Less is more.
- You want to tweet your viz (if you have twitter, then great, if not get twitter) and put it on data.world. I use the following format for my tweets
- For Data.World, you’ll want to post it on the discussion page and potentially the Viz Review page as well (I cannot recommend the review enough). You’ll want to post a PICTURE (not an embedded viz, dear god don’t embed it), a link to the tableau.public and perhaps a comment.
- Watch the podcasts. It’s surprising how much you can learn from the work of others, you don’t know what you don’t know and you’ll soon find lots of techniques and tricks you never imagined.
Feedback from friends and family
A somewhat less helpful form of feedback is to show your vizzes to those around you and ask what they do and don’t understand. What do they take away as the message of the viz? What do they see first? Is there anything that isn’t clear? It’s also quite useful to have someone unfamiliar with data visualisation as they can be a more difficult audience to get your point across.
Good luck with your application, don’t be afraid to fail a bit, but remember to learn. Don’t just, you know, fail for no reason.