Board of Trustees Treasurer, Courtney Irwin
“The third sector in the UK is extremely important, and extremely under-resourced.”
Pronouns She/Her
Roles Data Scientist at Kantar | Former Project Lead, Scoping Committee member, current Board of Trustees Treasurer
Links LinkedIn
How long have you been working in data?
I came to data science later in life. I used to be a project manager and worked in a lot of different sectors. I started as an IT project manager, then in international aid, and development projects overseas.
I really enjoyed working and playing with data and spreadsheets, and had initially studied some programming. I would make extended spreadsheets about our project management budget, tracking progress or monitoring and evaluation, checking that we were delivering against our goals. I liked to make lots of fancy formulas.
I thought data science would be something that I would enjoy, and saw an opportunity to work in lots of different organisations using that skill set. It became a deliberate decision to change my career. I studied a Master's degree in 2018 and then started working in data science from there.
I also did a three-month fellowship with Data Science for Social Good (DSSG), which was both fun and practical. It was to build an image recognition model for echocardiograms, videos of the heart. So using kinds of computer vision systems, working in a team, and also working with a hospital to talk to them about how this system would actually work in practice.
What advice would you give to someone getting started?
When I started, I felt that my technical capabilities were holding me back. It takes quite a long time to learn how to code in a particular language, especially to do it well. I was always nervous about the technical skills I didn't have. These days it probably feels less important to learn code, because you have AI tools that can write syntax for you.
But it drives home how important it is to grasp the more basic stuff, such as, are you answering the right question? When you come up with answers, do you understand what people will use this information for? Is it going to help people make decisions? Is it going to be truthful? Does it hide some bias?
You have to think less like a technical person and instead really understand these problems, what you're trying to achieve, the strategy, and the questions you're answering. It's the same for DataKind projects, and at work. Understanding the context of the problem is more important than efficiently creating a technical solution.
What DataKind UK projects have you been involved with?
I've known about DataKind for a long time. I first came across them at DSSG when the CEO at the time, Giselle Cory, spoke to us about the work DataKind UK does. I also used to go to their Ethics Book Clubs, back in the day.
I volunteered for the first time in 2020, during the pandemic, on an online weekend project with Action for Children. It felt like I didn’t contribute anything because the time went so quickly - a common newbie mistake! Since then I've been a project lead a couple of times and supported other new volunteers to avoid some of the pitfalls I faced. I was also on the Scoping Committee, assessing project submissions, for a year.
I’m now part of the DataKind UK board of trustees as the Treasurer, and at the moment I’m also developing a webinar for qualitative data analysis with the Head of Data Science and another volunteer.
What have you learnt as a volunteer?
I really enjoy learning about charities when I join projects, because it's not a sector I understood very well before I worked with DataKind. You learn how they operate, and the conditions under which they operate. In the projects I’ve joined, organisations can rely a lot on external data to inform some of their decision-making. Helping an organisation called Herts Young Homeless just outside of London in Hertfordshire, I saw how difficult it is working across Local Authorities to help young people at risk of homelessness.
In my ‘corporate’ life I don't work with these wider social challenges, or meet people who deal with them. Working with an organisation called Disability Huntingdonshire (DISH), I saw how people can spend six months waiting to hear about their benefits. I can’t imagine how hard that is.
I also loved an exploratory project with Inclusion Labs. They're a very small charity, but their staff were very engaged and they had a strategic plan for how to use their data in the future. That was fun, but it also felt very meaningful, a really beneficial outcome.
The third sector in the UK is extremely important and extremely under-resourced. I know how difficult it is for charities to hire data people. But there are a lot of incredibly impressive people who tackle significant social issues with very little support.
Is there a data project that inspires you?
There are really interesting and inspiring projects mapping air quality, such as Purple Air. You can buy devices to monitor the air quality near you, and then make this information available on open source data sites. If authorities are capturing data then they can have biases.
So when citizens supplement this data with their own observations, it's quite a powerful argument about the need to improve air quality. I haven't actually gone out to purchase an air quality monitor, but I’ve often looked at these maps to see who's measuring it, and it’s surprising how it changes in different locations.
Is there a resource you’d recommend to the community?
Kaggle is a good source for data, and also competitions are a good opportunity to have a go at machine learning algorithms. I don’t know what difference AI has made, but when I was studying it was important to learn a lot about algorithms. I used to listen to a podcast called Machine Learning Guide. It is just one man but he gave good explanations of different algorithms.
Tell us something non-data-related about yourself!
Outside of work, I've been learning Italian. I'm still terrible at it, but I try to study for 10 or 20 minutes most days. I also quite enjoy sewing. I've been making my own clothes since the pandemic. Always clothes for me, some trousers, t-shirts, jumpsuits. I make them for myself because I wouldn't want to force anybody else to wear my badly sewn clothes, but I enjoy the process. And then even if they don't look that good, I still feel satisfied wearing them myself.
There's some pride in putting on your own t-shirt, even if the neck is a little wonky, you know. There's quite a lot of problem solving in sewing your own clothes, especially when you make mistakes along the way and have to fix them. I can sit there with my little quick unpicker and unpick all my stitches. So I feel like it's forgiving. You just have to have the patience to go back and do it again.
