The aim of today's session will be to develop an understanding of the intersection between data and communication – where this works well, where this goes wrong and how to improve the communication from their projects. We'll look at how we can use data to tell compelling stories, change the way people view problems and effectively suggest a course of action.
You should aim to:
- understand avenues for communicating project results beyond the usual dashboard/graphs approach.
- understand the use of design principles to engage your audience and draw their attention
- be able to critique data presentations (both good and bad) in a variety of media
- gain experience at developing your own effective data-driven communication
Have a poke around online to find examples of excellent online examples of data-driven storytelling. You should also check out:
- ABC News Data team: https://www.abc.net.au/news/interactives/ Simon Elvery (a member of that team) has a great piece about the process of designing a data driven story about cellphone metadata here: http://www.walkleys.com/nerd-box-how-the-abcs-interactive-team-made-the-creepiness-of-metadata-personal/
- Guardian Data Blog: https://www.theguardian.com/data or the Australian version https://www.theguardian.com/australia-news/australia-datablog
- FiveThirtyEight: https://fivethirtyeight.com/tag/data-visualization/
- Nathan Yau/FlowingData is a goldmine of good dataviz: https://flowingdata.com
- Moritz Stefaner has some excellent project examples: https://truth-and-beauty.net/
- For an alternative take, check out the NYT R&D group: http://nytlabs.com
We'll cover visualisation and perception briefly in the course. For more details on the science behind how we perceive order and make decisions, take a look at Thinking Fast and Slow by Daniel Kahneman: https://www.amazon.com/Thinking-Fast-Slow-Daniel-Kahneman/dp/0374533555
The grand-daddy of visualisation is Edward Tufte, who was writing about this back in the days of moveable type, rather than electrons. His book Visual Explanations: Images and Quantities, Evidence and Narrative is the default text in the field and has arguably influenced more data viz practitioners than anyone else: https://www.edwardtufte.com/tufte/books_visex
Have a look around on the internet for examples of bad visualisation - there are plenty of sites with 'halls of shame'. One good example is How to lie with maps: https://www.ft.com/content/65b5df0e-49ff-11e8-8ee8-cae73aab7ccb
Todo: add pre-session reading and resources (and why you might like to read them - see week two's README as an example)