Dr Martina Pugliese
In this talk, I want to share the process and workflow I go through as someone who is approaching data visualization coming from the technical/scientific angle, and what I've been learning (and still do) about design and communication/journalism.
Data visualization is very much an intersection between the gathering of reliable and correct factual information (with all the machinery of data splunking and analysis + statistics) and its visual representation, which has to be not only appealing to the eye but also clean and to the point. I find that rather than using tools and libraries (which I still love and use anyway), drawing visuals by hand gives me the most freedom in crafting visual info the way I conceive it in my brain. Plus I enjoy drawing, even if my end results are not pretty nor professional - it's the process that matters to me.
I will do a little walkthrough of the messy line I follow when drawing a viz:
- getting excited about a topic;
- framing the question to gather data for;
- gathering said data (when I manage, I will talk about failures too);
- conceiving the representation and trying to execute it (the most delicate step).
I learn a lot along the way and I hope to teach others something too - from little facts about our world to bigger questions that need deeper investigations. See you there!
I work in data science, so I come to dataviz from the technical side - I have several years of experience in the field and a scientific formation (PhD in Physics), and a long-standing sweet spot for data. Because I've always loved measuring things and phenomena to extract quantitative info but also communicating results and sharing knowledge to any audience, I've started datavizzing various finds, teaching myself best practices in design and even data journalism. I've got a little project called "doodledatcard" (on Behance too) which aims at just that, where I hand-draw visualisations - the goal is purely educational, for both myself and for anyone who may find it interesting.
Martina is Data Science Lead at Edinburgh fashion tech company Mallzee (mallzee.com).
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