When I was going through the application process for to the Data School, I looked everywhere for stories from people who had done it. So now that I’m in, I want to share my experience.
I’m David of cohort DS51 — here’s how I got in, what I learned, and how you can hopefully do the same.
I was drawn to the Data School because it wasn’t just about technical skills — it was about becoming a consultant who could make an impact. The training, support, and real-world placements stood out to me, especially for someone like me who learns best through doing.
But beyond that, I saw it as a platform, not just to become “good at Tableau” but to build a career on my own terms. I’ve always wanted to blend data with subject matters that i am passionate about, and even though at the time of me writing this i am only two weeks in, the Data School has given me the tools to start making that happen.
My first project focused on analysing the powerhouses of football — looking at how club performance, spending, and squad value connect. But more important than the topic was what I learned while building my first dashboard.
Here are the biggest takeaways from the whole process in personal experience:
Choose a topic you care about
It sounds obvious, but it makes all the difference. You’re going to spend hours with this dataset — pick something that genuinely interests you and not what you think the data coaches want to hear. That passion will come through in your analysis, design, and especially in your first interview. There's no such thing as a bad topic!
Simplicity wins
It’s tempting to overcomplicate things to prove your skills. But clarity beats complexity every time. Having started my role I've appreciated how the Data School do not expect you to be a Tableau expert on day one, instead they start from the very basics just to build your technical confidence. This should be even more reassuring at the interview stage to just focus on telling a clear, focused story. Use fewer charts, but make sure each one has a purpose and relates to the previous.
Start scrappy, then refine
The hardest part is starting. I procrastinated for a while because I didn’t feel like i could commit to ideas that didn't feel 'worthy' of getting in, but once I began sketching ideas and playing with the data, everything moved faster. Don’t aim for perfection upfront — build, test and improve.
Final advice
If you’re thinking about applying, my biggest advice is this: just start. Whether you feel “ready” or not, every draft you create is progress. And the more personal and authentic your project is, the more it’ll stand out. Just show a true passion for data and willingness to learn and be coached.
And if you ever want feedback or someone to bounce ideas off, feel free to reach out.