Anatomy of a successful Data Schooler

by Megan Hunt

Following on from the last blog about the most challenging aspects of DS, Andy asked us to reflect on and write about how to be successful in the Data School.  I guess that really depends on how you choose to quantify success.  Being part of a team who get to learn from some of the best in the world at their profession sure feels like a fantastic place to be.  So here’s what I think you really need to be successful in the Data School, along with a few tips.

How to build a Data Schooler:


Top Tips

I’ve got 3 tips which I think will definitely help future Data Schoolers be more successful along their journey.


#1: Learning Style

Figure out your learning style as quickly as possible.  If you need to write stuff down then write it down, if you need to draw pictures then draw pictures.  Watch YouTube tutorials, read blogs, use Twitter to follow and chat to your favourite data viz people, make up gloriously silly puns to help you remember things, find a buddy who has the same style as you and explain things to each other, listen to podcasts.  Do it all.


#2: Feedback

Ask for it as often as possible, from everybody.  Take it and use it.  It will always be constructive and will always help you to improve.  Collect as many perspectives as possible.


#3: Sense of Humour

The Data School is hard work but my word it can be fun.  I don’t think I’ve ever laughed so much or so regularly as I have over the last three months.  The way through the inevitable frustrations which will come your way is to ensure that you keep your humour muscles as well exercised as your grey matter will be.


And Bonus Tip
#4: Sleep

This is more advice for life, but for me it is particularly pertinent.  Get as much sleep as you can when you can.  Sacrifice your Zzzzs for no man nor beast.  Also, Tableau and Alteryx and data in general will invade your subconscious and you will find yourself dreaming about them.  Nothing quite prepares you for (or is quite as strange as) dreaming that you are physically inside a bar chart or that you’re a dataset trying to blend with an Excel file on a common field.