4 Apr 2017

What I learned from teaching data journalism

I have been teaching an Introduction to Data Journalism module to a group of MA Journalism students at City University London this year.

They were a varied group of Interactive, Investigative, Finance and Erasmus students - of many different backgrounds, from many different countries and with many different interests. What united them was their interest in using data to help find new stories and improve their current storytelling.

Through teaching them some data-led techniques to help achieve this aim, I also learned about how best to start understanding the process of data journalism. It helped me clarify some thoughts I already had about data journalism, while also challenging some other assumptions I had settled into.


Build the foundation


It's important to build the groundwork first, and this involves finding data. How to source information, where to find open data and where to go if you can't find information on your subject.

There's no point giving your students data that's already been sourced and cleaned unless they understand how the data got to this point in the first place. They need to know what the starting point of data journalism is, and like all other strands of journalism, that's your source.

This means taking them through the process of using open data portals such as the World Bank's, as well as talking about how we can find our own data through scraping, information requests and other means.

Take it slow


Analysing data is the bread and butter of the practice. This is where we find our stories, how we prise valuable and engaging information from otherwise untapped and uninteresting data.

It's the essential - and fun - part where we find out what our story is. And so it's important that we invest the necessary time to look into this section of the data journalism process.

The variety of my students' skillsets when they started the course was surprising. While a minority had used statistical programmes such as R to crunch data, many more had come from arts-based degrees and were daunted by the prospect of "lots of numbers in a spreadsheet".

Accommodating the two to find a common ground in data analysis was key, and I thought it was the right decision to spend many weeks on different statistical analysis platforms in order to provide a variety of tools with which to analyse data.

Don't go straight in with the fun stuff


Almost everyone, when they want to get into data journalism, wants to learn how to visualise data. This usually involves wanting to create a pretty choropleth map or a complex interactive as soon as possible.

This, of course, is a mistake. It is useless in itself, unless it's twinned with visual and data literacy.

There are plenty of bad visualisations online and this is partly because of people who have the skills to build graphics but don't have the understanding of how to communicate statistics. I therefore spent the whole first term trying to help my students understand the best practices for visualising the data, without really going into detail on many different tools that could be used for doing so.

Simple and complete is better than complicated and unrefined


While, as said above, it's important to emphasise statistical literacy before going in with the "fun stuff", that's not to say aspiring data journalists cannot tell visual-led stories.

There's a lot that can be told through simple visualisations such as bar charts and line charts - and minor adaptations of these. And so while I focused on visual literacy instead of an arsenal of tools in my first term of teaching, I still highlighted some platforms for creating basic visualisation tools.

As then-Guardian Data Editor Simon Rogers has previously said, "anyone can do" data journalism. Introducing students - all keen to create many different types of visualisations - to free visualisation platforms such as Highcharts and Datawrapper allowed them to start producing data-led stories without getting carried away with overly complicated - and potentially flawed - visualisations.

This allowed them to practice with the basics first, learning the best visualisation practices while doing so, before moving onto more advanced (and fun) stuff.

What this says about data-driven journalism


None of these points above are ground-breaking, but they do reinforce an important point: you have to get the basics right first.

It's important to remember that the core of data-led journalism is in the analysis. In the finding of stories that other reporters couldn't find. In uncovering stories in vast quantities of information that the ordinary population does not have time to discover for themselves.

Data journalism can often be beautiful, attractive and technically brilliant, but none of this matters if the foundation isn't there.

Students will be biting at the bit to work on the huge interactive visualisations that are probably the reason they're interested the practice in the first place. But I think it's important to focus on the sourcing and analysis of data for first - as this is our starting point and the way that we discover groundbreaking stories.

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