A few weeks ago I announced that I was launching a new MA in Data Journalism, and promised that I would write more about the thinking behind it. Here, then, are some of the key ideas underpinning the new course — from coding and storytelling to security and relationships with industry — and how they have informed its development.

1. Not just data journalism, but data storytelling: video, audio, text, visuals, interactivity — and UX

In designing the course I wanted to ensure that students thought about how to tell their data stories across all media — not just text and datavis.

I created a central Narrative module which gives students the technical and editorial skills to report a story across multiple platforms and media. That includes video and audio, techniques of longform immersive storytelling, social media-native data journalism, and visual journalism techniques (“Overview, zoom and filter, then details on demand“).

The module also looks at how to employ narrative techniques in interactivity too — after all, what is the “user journey” in UX, but another narrative?

2. Coding in a journalistic context, not a computing class

It’s no surprise that I’ve decided to make coding-as-journalism a central part of the MA in Data Journalism.

We do not send journalism students to the Law faculty to learn Media Law, or to the English faculty to learn about subbing and style, so I wanted to ensure students learned coding in a journalistic context too.

Doing so means students get editorial, ethical and legal guidance in class alongside technical support. Hackdays, Hacks/Hackers meetups and other collaborations with computing and other faculties provide opportunities for cross-disciplinary innovation around shared objectives.

Equally importantly, teaching this way makes for a more efficient and pedagogically effective experience for the student: being taught how to make a for loop or generate a range of numbers within the context of writing a scraper or an interactive story makes for a much more rewarding learning experience than learning the same skill in an unrelated context (indeed, it’s why I wrote my books on scraping and spreadsheets).

A final reason for keeping coding teaching in-house is that journalists are ultimately judged on their reporting over their coding, and I felt data journalism teaching and assessment should reflect this: the point of the modules is not to merely demonstrate technical excellence, but rather to demonstrate how technical skills can be used to facilitate journalistic excellence.

Striking, original stories made possible through creative application of coding and other data skills will impress potential employers much more than something technically impressive but journalistically basic.

3. Three languages — and computational thinking

Teaching coding this way means I can introduce students to at least three different programming languages in at least three different modules across the course: R and JavaScript in the first semester, then Python for advanced scraping and other investigative techniques such as network analysis in Specialist Reporting, Investigations and Coding.

Teaching three languages allows students to learn the underlying techniques of coding which are language-neutral: being able to identify an editorial challenge using computational thinking, and then find the relevant libraries and examples which will help them to solve it (and understand the documentation).

It also means they can adapt to a newsroom which prefers one or more of those particular languages, or communicate with developers who use different languages.

4. (Re)inventing the data journalism workflow

Most newsrooms have gone through some form of reorganisastion in the last decade, and are likely to do so again in the next.

The introduction of data journalists or interactive project teams has often been part of that — but we still don’t know the best way to fit those data journalists into the wider organisation, or organise those teams.

We also need to be thinking about how the integration of data journalism and its workflows affect the journalism itself. To list just five questions facing us:

• To what extent are data journalists choosing to report on certain subjects over others because the data is more readily available?
• How does a journalist in a broadcast organisation work differently from one in a print or online-only publisher?
• When developer time is expensive and a bottleneck to innovation, how does that shape what can be done editorially?
• What automation can we build into our workflows — and what issues does that raise?
• And of course, how does the CMS limit us editorially — and how do journalists get around those limitations?

I wanted to make sure that they had an opportunity to explore these questions in practice as they organise their own newsrooms alongside students on the MA in Multiplatform and Mobile Journalism.

When in their later career they begin to form their own data units in media organisations, or are invited to contribute to yet another reorganisation, it’s important that they are able to make informed decisions.

5. Media law and ethics — and technical defence

I wanted the law element on the MA Data Journalism to not only address regulatory frameworks, but also specific considerations when dealing with data. That includes information security, the ethics around issues such as personalisation and mass data gathering; legal considerations such as data protection; and the use of laws such as Freedom Of Information.

It is one of the pecularities of our age that it is no longer enough for a journalist to be able to mount a legal defence to protect their information and their sources; they must now be able to mount a technical defence as well.

6. Specialist and investigative skills alongside technical skills

Most data journalists operate much as specialist or investigative reporters do: focusing on a particular field and trying to understand how information is collected and stored within that.

I wanted students to have an opportunity to develop that specialist knowledge, and exercise data journalism skills alongside other important techniques such as analysing company accounts, interviewing, and understanding how a system works.

After all, a data journalist can only work with the data they have access to. And having good data often means knowing where to look, who to ask, and how to understand the context in which it has been gathered. This part of the course also provides an opportunity to create striking original reporting which builds the student’s reputation.

7. Working with industry — and communities of practice

Many current data journalists arrived in their roles through internal routes having worked in a freelance role on particular projects that needed data skills and those roles either being extended or made permanent.

I regularly field calls from media organisations asking for students with data journalism skills to help with a story, and so for the MA in Data Journalism I worked to formalise those relationships in a range of ways.

These relationships cover local and national newspapers, magazines, broadcasters and online-only publishers both in the UK and internationally.

It means that students have access to a range of opportunities to work on industry projects, and can easily seek out potential clients whose problems they can take on for the module addressing enterprise and entrepreneurship. The idea is not just to create opportunities for students — but also hopefully building capacity in the industry itself.

Just as important as industry are the wider communities engaged in data journalism (more here): a lot of research has been done into the intersections between journalism culture and hacker culture, and I believe that it is important that data journalism students engage with the various online networks surrounding data and coding.

Those are the communities which will support the student long after graduation, as new tools and techniques come along — and new stories.

I’d welcome any thoughts on the course and other elements which should be included.