Mastering data for good

As fun as it looks, I don’t aspire to be a data journalist. Yet in September last year, I began a Masters in Computational and Data Journalism.

I had a career shift in mind, so I quit my job and wrote a (too long) list of things I wanted to learn to prepare me for the change. The Cardiff programme most resembled that list.  With courses split between Cardiff University’s Computer Science and Journalism departments, we covered the full stack from data access and analysis, to front-end dev and visualisation.

But it was still an ill fit, because what I wanted didn’t exist. Since then, I’ve thought a lot about my wish list for that dream programme: a Masters in Data for Good. Here’s my working draft:

  • The scene: What *is* data for good? What does the data for good scene look like in the UK? What does it include and how has it developed? What do other countries do, and what can we learn from them? This might include a tour of the different sectors striving for social good, and an overview of social policy goals
  • Ways of working: Data for good tends to involve more than one type of organisation, and often involves volunteers. What’s the trick to managing projects across private/non-profit/third sector partners? How is volunteer management different from that of paid employees? How do you create a self-sustaining community?
  • Making change happen: The most effective data for good projects are those with a living legacy. They have a lasting impact on the way an organisation uses data, and permanently up-skill those involved. What is the best way to achieve this? In short, how do you usher in lasting organisational change?
  • Ethics: How do we best tackle the ethical challenges associated with data collection and analysis? This is essential for every data project, but particularly so for those involved in data for good. The community has a role in building trust in data and its uses, and ensuring we protect our right to collect and access data when those rights are correct and appropriate
  • Communication: Data science doesn’t go far if you can’t explain what you’re doing and why. So how do you talk about data to non-data people? Ensuring the explanations are accessible and transparent is particularly important given the ethics point above
  • Data science:  Last but foremost…. Are there common data challenges across the social sector? If so, what are they? What are the data science techniques that are most effective in different situations? How do you make sure the work is useful, not just beautiful? Those doing data for good have a wide range of skills and specialisms, and no course will be able to tackle them all. But a good grounding in data access, analysis and visualisation would be a decent foundation

Some of these elements are not special or unique to data for good, and could be pulled in from other programmes – e.g. data ethics should be a cornerstone of any programme about data. Other elements are unique and would need to be made to measure.

Creating this programme would be a challenge – not least because you’d probably have a  small, mixed audience with different skills/experiences in different areas, and you’d have to keep up with a fast changing data for good world. I reckon it’s a challenge worth taking. You? Let me know your thoughts.


Icons in header image from Flaticon

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