Start Here: A Dataviz Crash Course, Without The Crash Out

Data visualization is a series of decisions. Each one contributes to making the full picture. What should I visualize? What should the title be? Is this the right chart type? Right colour? Right question? Do I even need a chart at all?!

The process can be exhausting and confusing... and still lead to some questionable charts.

As a perennial overthinker and certified dataviz nerd, I've thought about these questions far too often (still do), but have gotten faster and better at answering them. This workshop will help you get started on your own visualization journey and devise your own way to overcome decision fatigue when making charts. I don't hold all the answers, but this workshop can serve as a portal through which you can get to them.

Who is this for?

This workshop would be helpful for anybody new to data visualization: students, early-career professionals, journalists, researchers, practitioners who want to include dataviz in their work, but don't know where to begin, or tried and gave up because of the confusion. Whether you feel you don’t have enough information—or have too much and don’t know where to start—this workshop is designed for you.

Why me?

Once I discovered dataviz, I was hooked. I knew what I wanted to do, but had no idea how to do it. Dataviz has no set roadmap. Years of gleaning the internet, attending lectures, volunteering at conferences, reading books, bookmarking resources, and making charts has helped me hone my own dataviz bone (and muscle, and instinct). I've been the confused beginner who didn't know where to start. While I can't coalesce all of dataviz into a single workshop, I can share the decision-making practice, mental models, and tricks that I've learnt (and borrowed) from the best in the biz and adapted to my own workflow. This is the workshop I wish I had 8 years ago.

Workshop Flow:

1. When do I need dataviz? Exploring the right and wrong reasons to make a chart.

2. First round of decisions: The data, insight, and chart Participants learn to tap into their curiosity and identify questions to ask their dataset. They then pick up a paper and pen to sketch a first-pass visual. The focus is not perfection, but understanding what relationship they are trying to show and why.

3. Second round of decisions: The title and the colours We examine how the framing of the title and choosing of colours changes interpretation.

4. The good (practices), the bad (decisions), and the ugly (charts) We look at a few examples together and unpack the decisions behind them. The aim isn't to ridicule, but identify what worked, what didn’t, and why in a systematic way.

5. Make your worst chart Participants are urged to intentionally create a "bad" chart. As bad as they can. This is to lower the fear of messing up, to reveal hidden assumptions about design rules, and use the techniques learnt to critique charts.

6. Checklist The primary learning outcome is a practical decision checklist the participants co-create that they can take back to their own workflow.

7. Thinking out of the box and off the charts We briefly explore breaking the rules and trying new mediums—like data physicalization, sonification, and visceralization.

Behind every chart is a chain of decisions. Let’s make them intentional!