This article will go over insights to help you prepare for each step of your application.
The application for the Data School is quite unique in that the first step is submitting a visualisation of data created from Tableau. If you have already done some work on Tableau this can be really just be anything that is of interest to you. On the other hand, it can be quite daunting if you have no previous knowledge or just started to learn Tableau. In that case, I recommend picking some of the sample data available here: https://public.tableau.com/en-us/s/resources it also has the added benefit of having many previous public visualisations.
However, for those brave souls that have previously done some work on Tableau, I recommend you challenge yourself. There are plenty of creative and fun datasets like – but not limited to – Pigeon Racing, Chopstick Effectiveness and the effect of LSD on Mathematics found here: http://blog.yhat.com/posts/7-funny-datasets.html and also for a large collection of free data: https://www.kaggle.com/datasets
The main reason I stated this is to try making it a learning experience, and experiment with new ideas and designs. I definitely encourage you to message the coaches at Data School (who may or may not respond on Friday afternoons) a little earlier to help provide valuable insights.
If you haven’t already at this stage make sure to incorporate the feedback the coaches have given you. In which will believe me, will definitely increase your chances of making it through. At this stage, I wouldn’t recommend mulling over it too much and over-practice. Instead you should run it through your friends and family, especially those that might not have a technical background and see if they can easily understand your viz or the way you explain it.
Giving a presentation and interacting with the coach live also means that you will probably be tested your understanding of the data. I would make sure to at least know what each of the basic variables presented in your dataset are and what your visualisations are actually representing. If you would want some more ideas on the visual aspects of your viz visit the Makeover Monday Gallery page here: https://www.makeovermonday.co.uk/gallery/
If you have made it to this stage, then give yourself a pat on the back, you probably resisted the urge to put a pie chart or a sankey chart. My recommendation is to start early as again, there is feedback readily available. It also gives yourself time in between and coming back might give you a fresh understanding and new ideas. If the dataset is huge there is not any need to incorporate all of it. Just being able to draw out around 3 insights is already more than plenty and I would argue why would you want extra work for yourself?
You should on a story and plan it out (heavy emphasis here). This is just as important if not more than demonstrating your technical abilities. In fact I personally think anyone can really follow the steps for a lot of the technical aspects but story-telling not something that can be easily learnt. More on storytelling with data here: https://hbr.org/2013/04/how-to-tell-a-story-with-data
Some simple things to avoid:
- Filling up the dashboard with as much as possible and using twenty different colours (its not a Picasso painting)
- Trying to force a false narrative and not using zero as your axis (you might find yourself ending up in the infographics team for your local newspaper)
- Overuse of fancy visualisations (only marginally better than using a pie chart)
The dataset I received was based off US consumer data spending. These involved larger numbers so I decided to normalise it to one hundred dollars as the total amount spent. This results in much easier to digest numbers as well as a much more relatable story in how someone would spend $100 has changed over time. I also put the years into decades and fit in some research (unrelated to the data), to help identify why people’s spending changed each decade.
Link to viz (Please follow me I don’t have many): https://public.tableau.com/views/DataSchoolShort/100USD?:display_count=y&:origin=viz_share_link
One of the most important things is actually understanding what the Data School is and articulating your reason for joining it. While it might be tempting to answer “I love free alcohol” that only gives you a 75% chance of making it through on the spot. The other 25% chance comes through do a bit of research on the company culture and goals. There isn’t much other advice here other than its not quite like other interviews, its best to just be yourself and act naturally as The Data School embraces people of all different backgrounds.
And of course, the most important part is as aforementioned – learning whilst completing the tasks and from the wonderful and detailed feedback. At the end of the day simply enjoy the process and don’t think too much about having multiple professionals stare you down you do your presentation.