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"In a world where governance (and so much more!) is increasingly advised by data insights, being ‘data literate’ is an incredible strength."
Why did you choose the Q-Step programme as part of your degree?
By chance! I had originally enrolled in the BSc Social Sciences. On my very first induction day at UCL, I met someone (who, funnily enough, was to become one of my best friends at university) who told me they had enrolled in the Q-Step programme. They told me they had chosen the Q-Step programme because of the quantitative methods background it set to provide to students. Back then, I was particularly interested in Economics, and thought that putting an emphasis on quantitative methods as part of my degree was particularly relevant. I have no regrets: the programme taught me a lot - beyond my expectations.
What was the highlight of your programme? What was the biggest challenge?
I don’t think the strengths of the Q-Step programme can be encapsulated into a single highlight, but rather a combination of them. In my opinion, growing literacy about data (data handling, data analysis, data science) is, regardless of your background or future aims, fundamental. It enables you to read between the lines, and develop a critical understanding of what’s around you: be it in the news or the scientific articles you are meant to read as part of your degree. In a world where governance (and so much more!) is increasingly advised by data insights, being ‘data literate’ is an incredible strength. Doing Q-Step at UCL also means being taught by dedicated, insightful people on the newest, cutting-edge techniques for data analysis and science. It might also mean being ready to commit to stepping out of your comfort zone! In my case, I had never done much programming - in fact, I barely knew anything. As part of Q-Step you are taught to program in R- it is really like learning a new language! Challenging, but hopefully, rewarding. Another point worth mentioning - which I consider to be a strength of the Q-Step program as much as a potential individual challenge, is the way in which Q-Step bridges the gap that exists in being taught quantitative methods for the social sciences. Aside from the outdated and rigid conception of what doing ’science’ entails, there is absolutely no reason for social scientists to be less familiar with quantitative reasoning and methods than anyone else. Although there are undoubtedly limits to using quantitative methods for the social sciences, being able to understand them empowers you in your choice of using these methods, or not.
How did the Q-Step experience influence your plans following graduation?
My Q-Step experience completely influenced my after-graduation plans! During the first semester of my second year, I was taught the premises of data analysis for geographical data. I really loved it. Shortly after, during the summer between my second and third year, I completed an internship in a social enterprise which uses mapping tools and methods on a variety of citizen-science related projects - and again, I loved it. I developed very good relationships with my managers, and it pushed me to take the decision to keep on using data science and analysis throughout my career, in one way or another. Besides, the teaching team I was taught by as part of Q-Step really helped me to approach quantitative methods in a critical manner - an approach I wish to keep in the future. These ideas and influences mixed together, and today my plan is to go into Human Geography, hopefully using a range of qualitative and quantitative methods; a plan I would have never made if it was not for going into the Q-Step programme.
