Top Three Application Tips from Current Data Schoolers

by Sophie Sparkes

On 16 March we ran an 'Applications Special' Meet and Greet event where current Data Schoolers talked about their Data School applications and shared their top application tips. This blog post was written by Henry Mak one of the Data Schoolers who presented his top tips. You can watch the whole Application Tips Special session on YouTube.

Looking for tips for your application to The Data School? You’ve come to the right place! A few Data Schoolers recently shared some of their advice to help you make your application dashboards as strong as possible. In this blog post, we will go over their top three tips and more.

Top Three Tips

Tip 1: Get feedback

In order to improve what you’ve created, you must get feedback. Get feedback from as many people and from all sorts of people, whether they are an expert in your topic or not. It also helps to get critique from anyone who may not have high data-literacy, to see if they understand what you’re trying to convey. Another alternative is to utilise the large and active #datafam community on Twitter, who will surely lend you some of their eyes. Remember that current Data Schoolers and The Information Lab will always provide you with feedback, if you ask.

In any case, don’t be such a perfectionist that you don’t submit your work for feedback until it's 100% done. It’s often better to seek feedback earlier and more often.

Tip 2: Interrogate and understand your data

One of the components being tested are your analysis skills. A tip to make this process easier is to interview your data and really dig into it. For example, you could use the 5 W’s and 1 H technique where you come up with questions starting with:

  1. Who…
  2. What…
  3. When…
  4. Where…
  5. Why…
  6. How…

Tip 3: Sketch it out

Go old school and sketch your ideas out! Whether on paper, on a whiteboard or digitally – these sketches don’t have to be magnificent. By creating sketches you can see how everything lines up in the way it looks and it also allows you to quickly pivot your ideas. Remember, there’s usually more than one way to visualise the data (although some are more effective than others). Finally, sketching can also assist you in creating a coherent narrative flow for your dashboard.

Even More Tips!

As well as these top three tips, here’s a collection of the other tips from our Data Schoolers

Alisha Dhillon

  • Create a dashboard on what you know. Your passion really translates in your work and also your interview
  • If you want some inspiration, search for 'dashboard week' on the DS blog
  • Give credit, for example the data set source, in the footer of your dashboard

Garth Turner

  • Your colour choices make a lot of difference and there’s a lot of things to consider, for example how it looks, and also colour blindness. If you're not confident that your current colour palette works then use a colour palette generator tool like

Henry Mak

  • Get to know your data and really understand the context. Try to become a subject expert and read around the topic. Doing this will help out your analysis and your confidence in the interview. You may even discover something new to explore/investigate in your data
  • Don’t cram your dashboard with text. It should be short and informative. This is due to the nature of the interview where it is a presentation rather than you reading what you have written

Adam Ratcliffe

  • Creating a complex dashboard doesn’t necessarily mean it’s better. What you should aim for is a clear visualisation that is easy to understand and answers the questions you're asking of the data. Don’t underestimate a bar chart, scatter plot, or line chart
  • Understand and remember that feedback isn’t personal. You can always ask for clarification on feedback if you don't understand it, or want to know the reason why that specific bit of feedback was given

Hannah Murphy

We hope these tips have been helpful. Good luck!! If you made it this far, then we think you’d enjoy some of the links given below.

Useful links:

Alisha: First Stage Application | Tableau Public

Garth: First Stage Application | Tableau Public

Henry: First Stage Application | Tableau Public

Adam: First Stage Application | Tableau Public

Hannah: First Stage Application | Tableau Public
Other successful applications


Sophie Sparkes

Wed 01 May 2019