How it went
I was definitely a bit nervous starting my first proper job, but those nerves didn’t last long, and after meeting my cohort and coach, I felt much more at ease. We kicked off the week with introductions, a few icebreakers, and then a session with Tom Brown. He shared the story of how the Data School came to be, along with some travel stories from Vietnam.
I’ve already started to feel close with my cohort. We’ve bonded over a shared love of food, music, and Uno!
What I learnt
On Tuesday, we had our first full session with our coach, Serena Purslow, who walked us through the fundamentals of data structures and terminology. Coming from a Chemistry background, a lot of this was completely new to me, but it felt good to be learning. We explored different ways data is stored, from flat files and databases to warehouses, lakes, and lakehouses. We also covered some core concepts like ETL (Extract, Transform, Load), facts and dimensions, star schemas, and logical data models. It was interesting to learn how data is broken down, stored, and connected through relationships.
Later in the week, we spent a whole day with Robbin Vernooij, focusing on data preparation. This included cleaning and reshaping data, as well as understanding how to deal with messy datasets, whether that’s splitting columns, renaming headers, fixing typos, or tackling missing values. At first, it was a bit confusing getting to grips with all the terminology, but once we started working with Tableau Prep Builder, things began to click. Practising things like joins, pivots, unions, and filtering helped me see how data can be transformed to suit a particular question or goal.
We also discussed granularity, and how it affects the level of detail in a dataset. By the end of the session, I’d started to get a clearer sense of how to take a messy dataset and turn it into something tidy and usable.
On Friday we consolidated our knowledge from Robbin with Lorna Brown using Tableau Prep Builder. We explored the importance of planning our workflows through four key stages: understanding our data, defining the desired output, planning transformations, and then building the flow. We applied this to a task that involved combining three datasets to analyse profit per unreturned item. This involved data cleaning, pivoting, creating calculated fields, and joining tables based on logic. We also worked on a second exercise that involved comparing store sales to targets using calculated fields and joins, which reinforced how useful Tableau Prep can be for real-world data preparation.
What I’m excited for
I’m excited to dive deeper into this new world of data. There’s so much to learn, and I’m especially keen to explore data visualisation in Tableau. One of my goals is to learn how to build a violin plot, but I’m also looking forward to getting creative and seeing how different visual tools can help create stories out of data.
I’m also looking forward to meeting more people across the business. Everyone I’ve met so far has been really supportive, and it’s already clear that questions are encouraged, which is great, because I’ve got plenty!