Podcasts are a great way to keep inspired and up to date with what's happening in the world of visualization, statistics and data science - all while you walk, ride the bus or train, or even cook dinner.
Disclaimer: I am hopeless at keeping this blog up to date, and I have a long list of posts that I'd like to roll out. This list of podcasts and a post discussing them has been kicking around for a while, and today I read this post by Andy Cotgreave. I immediately thought I shouldn't bother to write this now, and then thought again... Why not? It aint hurting anyone to hear this twice ;-) So thanks for the kick up the arse Andy!
I do the odd presentation at companies about data visualisation, and I often get approached after with questions about how I got started, how people can learn more about visualisation, and what people could do to find out more themselves.
I always recommend a few books, as well as downloading and installing R and finding some tutorials online to play with. Increasingly there are also some great podcasts to listen to, and here are four that I'd recommend;
I love this podcast so much. It's about visualisation and a whole lot more. Hosted by Enrico Bertini and Moritz Stefaner, its focus stretches beyond the world of visualisation (which frankly feels like a circle jerk sometimes) and stretches into data science, policy, art, journalism, theory and accademia. Each week is packed with inspirational gems. Over this past semester, there has been a few times where the material and guests of this podcast has helped elevate some of my sketchy thinking into a sharp argument. You know those moments when an idea is kicking around your head, but it hasn't quite taken shape yet, and you are hunting around for a framework or model to help craft your thoughts into something stronger? Well, Datastores came to my rescue twice this semester, the most important being the interview with Mushon Zer-Aviv and his discussion about Stuart Hall. To cut a long story short, I'd spent some time that day playing around with some ideas about algorithms (not visualisation) and had this rough "sketch" of what I wanted to say about how we relate to them. Talking about how people "read" data or a visualisation, Mushon introduced a framework from Hall (from the 70s - hell yeah!) and it was one of those PING moments where I knew I had to investigate the framework further as it felt like a way I could hang and tie my argument together. Sure enough, some time reading Hall the next day in the library proved fruitful.* (see more below)
I cannot recommend this podcast enough. I always finish listening feeling inspired and refreshed.
It's not about the picture. That's the lesson that I have learnt over the years of drawing pictures with data. The arguments that always bore me senseless in visualization is about the "rules" of visual idioms. As soon as I start reading a blog post that starts to criticize someone because they use a pie chart my eyes start to glaze over. Yes, pie charts are often not the best idiom to use, BUT sometimes they are. I hate the black and white nature of a lot of the narrative that kicks about the field. It reminds me of dumb books like "The Rules" that make people feel safe and comfortable if only they have a checklist to follow.
Which brings me to What's the Point, (and you may be wondering this very thing right now...sorry, I ramble) which is a podcast produced by FiveThirtyEight. Each edition deals with numbers, and how data can be used (or misused). They also tackle issues around privacy, analytics, planning, business, politics, society...all the big stuff and look at ways these things are being driven by data (or could be driven better). Listen to this, as visualisation is not just about the picture. It's about the data; where it's from, who collected it and why, how it's been prepared, cleaned, aggregated, and analysed. So many steps before we wrap it up in design. And FiveThirtyEight is one of the organisations who really do this well.
Produced by the BBC, this podcast looks at claims, and the numbers that sit behind them. It's a great show at learning how to be savvy about claims that we may take for granted. They do the digging for you, and show how numbers can be twisted, then presented as fact. This loops back a bit to Hall's framework and negotiated reading. In order to be "good citizens" we need to engage with data a bit more, and question sources, intention and what is spoken to us as "truth". They look at how people derive numbers, and if they have used a valid or sound method to do so. This is a really important skill to have in order to not only produce visualizations, but also when you are "reading" them critically.
Two other podcasts I listen to that you may also enjoy are The PolicyViz Podcast produced by Jonathan Schwabish, and Data Skeptic. PolicyViz looks a lot more at the people making visualizations, and the broader field of data. Data Skeptic does what it says on the packet, and is a refreshing examination of biases we may have and debunking them via scientific scepticism and...DATA.
*In case you are interested in this, here is an excerpt of my essay. It was about how people engage and relate to algorithmic encounters in their lives. I discussed how many people believe that algorithms just "happen" when in fact they are loaded with human meaning, intention and creation. I was also interested in the huge range of views held by people about algorithms - powerlessness, disinterest, fear, trust/mistrust, disengagement...etc
"Referring to the work of Stuart Hall, who wrote about about the three stages of receiving or decoding information or communication, Zer-Aviv talks about the context of this framework today. The first stage is what Hall calls Hegemonic Reading; where a reader merely receives and does not engage critically with a communication or text. The second stage Hall calls Negotiated Reading, whereby the reader accepts a text, but could potentially modify, alter or reinterpret it after critically engaging with the text. The third stage of Hall’s framework is the Counter-Hegemonic Reading, where the reader will understand the text but reject it outright, and from an oppositional viewpoint (Hall 1973)."
From my essay for Data, Algorithms and Meaning - "Speculative Analysis of a particular data context" June 2015.
Hall, S. 1973, Encoding and decoding in the television discourse, Centre for Contemporary Cultural Studies, University of Birmingham, Birmingham West Midlands.
Zer-Aviv, M. 2015, 'Disinformation', in E. Bertini & M. Stefaner (eds), Data Stories, no 55, accessed: 13 June 2015.
My argument was all about how to get people knowledgeable about algorithmic encounters (when they are happening and what they are doing), and be able to engage in debate about what they mean and their impact. This deserves a bigger post so will just leave this idea dangling here... :-)