I’ve now been teaching data journalism for over a decade — from one-off guest classes at universities with no internal data journalism expertise, to entire courses dedicated to the field. In the first of two extracts from a commentary I was asked to write for Asia Pacific Media Educator I reflect on the lessons I’ve learned, and the differences between what I describe (after Daniel Kahneman) as “teaching data journalism fast” and “teaching data journalism slow”. First up, ‘teaching data journalism fast‘ — techniques for one-off data journalism classes aimed at general journalism students.
In this commentary, I outline the different pedagogical approaches I have adopted in teaching data journalism within different contexts over the last decade. In each case, there was more than enough data journalism to fill the space — the question was how to decide which bits to leave out, and how to engage students in the process. Continue reading →
Some useful frameworks for judging data from archival field outlined by @JamesLowryRAI at #datajustice18 in relation to Kenyan open data – including provenance (in that case opaque), custody (undocumented) and curation (no processes noted)
Until last month I hadn’t heard of diplomatic studies. It’s the discipline of studying historical documents, and comes from the word ‘diploma’, as in ‘verifying that someone hasn’t faked their records’ (I’m paraphrasing here). But this discipline of verification has some useful lessons for journalists — particularly data journalists — because it provides a very handy framework for picking apart what makes a record (data) credible, and what we should be looking out for when establishing that.
Particularly useful are three terms that are used to distinguish different aspects of a record’s credibility: authenticity; reliability; and accuracy.
This week’s GEN Summit marked a breakthrough moment for artificial intelligence (AI) in the media industry. The topic dominated the agenda of the first two days of the conference, from Facebook’s Antoine Bordesopening keynote to voice AI, bots, monetisation and verification – and it dominated my timeline too.
At times it felt like being at a conference in the 1980s discussing how ‘computers’ could be used in the newsroom, or listening to people talking about the use of mobile phones for journalism in the noughties — in other words, it feels very much like early days. But important days nonetheless.
Ludovic Blecher‘s slide on the AI-related projects that received Google Digital News Initiative funding illustrated the problem best, with proposals counted in categories as specific as ‘personalisation’ and as vague as ‘hyperlocal’.
You may have seen a cute little Twitter hack — popularised by Andy Baio — which allows you to roll back the years and recreate a decade-old Twitter timeline. The twist is that you’ll be seeing updates from people who you may not have been following at the time but discovered later.
Nostalgia aside, the same technique could be used by journalists to track what was being said by any particular group of interest at a particular point in time. Here’s how. Continue reading →
Sometimes an organisation will publish a spreadsheet where only a part of the full data is shown when you select from a drop-down menu. In order to get all the data, you’d have to manually select each option, and then copy the results into a new spreadsheet.
It’s not great.
In this post, I’ll explain some tricks for finding out exactly where the full data is hidden, and how to extract it without getting Repetitive Strain Injury. Here goes…
To get the data from this spreadsheet you have to select 51 different options from a dropdown menu
One of the industry partners for the MA in Data Journalism is Haymarket Automotive (What Car?, PistonHeads and Autocar) — we’re now inviting applications from people who are particularly interested in studying data journalism in relation to the automotive sector. In other words, data motoring journalism!
You should have a passion for journalism and retail journeys, cars or the car industry, be interested in helping find new sources of data for stories, and working on stories based on data collected by third parties, and have lots of ideas that tap into the power of data-driven journalism.
Editorial director Jim Holder explains:
“The automotive industry is awash with historic data, from car specs to buyer behaviour, and populated by experts who believe they know how to produce and read it. But our brands – and buyer’s guide What Car? in particular – have unique access to live data from in-market car buyers. Harnessed properly, the data has the potential to surprise and delight the car industry, and car buyers – and shake-up outmoded suppositions and attitudes.”
Successful applicants approved by Haymarket will work with a Haymarket Automotive brand during part or all of their MA studies.
If you are interested, please apply through the course webpage specifying in your supporting statement that you are specifically interested in working with Haymarket Automotive.