I have recently joined the DS12 cohort this week. The one thing I really wished I had available when I was applying back in September was at least a description of what someone who’d been in my shoes had previously experienced with their Data School application. So, here’s my application experience when I applied for DS12.
I had recently graduated from the University of Warwick with a BSc in Systems Engineering back in July. As much as I did enjoy studying engineering at university, I was pretty certain that I did not want to pursue engineering as a career. However, that also meant I had no idea what I had wanted to do. So, I decided to do what every typical STEM graduate steer toward to when they do not want to pursue their degree-related roles; either join a bank or go into consulting. I always blindly said that I wanted to pursue consulting without actually knowing what a consultant really does nor why I actually wanted to do it. I mean does anybody really know?
I was open to any and all opportunities. I found out about The Information Lab and their Consulting Analyst role on GRB. As soon as I read the job description I knew I had to apply! I’ll admit the main feature that caught my attention was that The Information Lab did not require you to have any experience beforehand whatsoever.
No CVs needed at all. Perfect! Why was it perfect? Simply because I had no relevant prior experience that I could talk about or experience to evidence my passion for the role.
The only thing applicants had to do was install Tableau and produce a viz with a dataset of our choosing. On top of that, we can send in draft vizzes to the Data School and receive (multiple) feedback on how to further improve before submitting our final piece of work.
Since I am an avid films buff, I decided to choose a dataset on The Academy Awards and investigate what factors contribute to films winning the Best Picture award over a 20-year span. I cannot emphasise enough how much I think it’s better to choose a dataset that you are genuinely interested in! While there is nothing wrong with choosing any odd dataset, from personal experience I do not think I would’ve been as interested and invested into learning Tableau as I’d been had I not chosen The Academy Awards dataset. I even went as far as adding extra information into the dataset. Yes, you can add data entries into datasets in your application!
After plenty of iterations and constantly improving my viz, I managed to make it through to the phone screen stage. The phone screen stage is simply you demonstrating your viz in a Skype-like call and talking about what you’ve learnt from your viz. You do not have to talk about exactly what you did Tableau-wise because they most likely know what you have done. After that, passing through the final interview stage means that you will be given a dataset (everyone receives the same dataset) and have approximately two weeks to prepare a Tableau viz to present at the Data School.
Advice that I would give is to make sure you are able to receive and address feedback well! One thing I had to learn was that while all your graphs, charts and analysis will make sense to you, it may not always make sense to others. Also, if after receiving feedback, you come to the conclusion that you may have to lose certain graphs or images that you spent ages working on (like I had to plenty of times) and you find yourself trying to “make it work”, just ask yourself if it is really adding extra value or is it just for show?
If it does not add extra value, then it is probably just best to get rid of it.
As you can see above, one of my first drafts for my final interview had a few images of London borough coat of arms that I initially loved because I simply thought it added a bit of pop to my viz, but then after receiving feedback from the Data School I was told that they were not entirely sure what value the images of the coat of arms added other than it just being there. I’ll admit I was slightly disappointed because I loved having the images there, but it did make sense! So, I got rid of the images, albeit reluctantly, and then realised that it did make my viz look tons better! Less is more.
You may not always like the feedback that you get, but that is the world of work. Most important of all, feedback is given for improvement, nothing less.
Good luck to all those who are applying for the Data School!
If you would like to find ways to practice vizzing on Tableau then you should consider participating in Makeover Monday! A lot of successful Data School applicants had used Makeover Monday to help their learning experience with Tableau.