π Hi! β One of the most common questions I get is from people wanting to break into data analysis or dataviz, and wondering where to start.
It's a tough one to answer. There are so many moving parts, and honestly, I'm not sure I'm the best person to give a definitive answer. β β
What's sure
But one thing is obvious to me: you need a way to prove to the world that you can take a project from A to Z and deliver a polished, professional outcome.
That's why I always tell people to create a GitHub account and push a few good-looking projects to it. Take my Data to Viz project as an example. It's fully open source on GitHub, the website looks great, and it has brought me a lot of job opportunities. β If you check my GitHub profile, you'll see 99 repositories (= projects) showcasing my skills. I never have to write a resume. I show this instead π! β βnote: In the AI era code becomes easy. But understanding how to use Git and Github remains a key skill to master (and I teach it here btw).
The next question that usually follows: ok, but what kind of project should I do?
An idea for you
The Pacific Dataviz Challenge is about to kick off. β It's a free, open-to-anyone competition that invites you to turn Pacific open data into compelling visualisations, with more than $15,000 USD in prizes across static and interactive categories. β The 2026 edition is themed around climate change, and the official datasets will be published on 1 June 2026 when the competitions open.
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I won the challenge two years ago. You can check out my project here, the GitHub repo here, and a short write-up where I explained how I pulled it together in 48 hours.
This year, I'm on the jury! So I'm trying to spread the word, and I'd love to see your submission. β If you're enrolled in one of my dataviz courses, I'll also set up a dedicated Discord channel so students can help each other out. Just a heads up: I won't be judging your work directly.
Do it!
You can read all the books and take all the courses, but the only real way to get better at dataviz and data analysis is practice.
And with AI making everything easier, there's no excuse. Take a few days, build something insightful, and put it out there!
Yan β βPS: planning to participate? questions about the challenge? Please hit reply! β βPPS: I'm planning to write an issue with all my tips to get a first job in the dataviz space. If you managed to do so, please let me know what made a difference for you! π β
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