Applied Social Data Science (M.Sc.) postgraduate personal statement example

Personal statement example

At the public library where I work, every enquiry at the desk is recorded with a pencil mark on a paper tally sheet: directions, printing, computer help, benefits forms, "other". Each month a colleague totals the marks and sends the figures to the council. After a year of making those marks myself, I became curious about what they leave out. A question about printing a form often turns into forty minutes helping someone set up an email account to apply for housing support, yet it counts as one mark under printing. The numbers are not wrong, but they describe the desk rather than the people at it. I want to study applied social data science because I would like to learn how to build measurements of social life that are honest about what they capture, and how to analyse them properly.

My undergraduate degree in sociology gave me a grounding in research design and a strong interest in housing. For my dissertation I collected around four hundred letters about housing published in two local newspapers over ten years. I coded them in NVivo for themes such as blame, fairness and who was described as deserving, then exported the codes and used R to compare how often each theme appeared across the two papers and across time. The analysis was modest: cross-tabulations, chi-square tests and simple charts. The most useful part was writing a codebook detailed enough that a classmate could code a sample of fifty letters independently. Where we disagreed, the codebook usually needed tightening, and that taught me that classification is a methodological decision, not a clerical one. I would now like to test whether approaches such as topic modelling could handle a much larger collection, and to understand their assumptions well enough to know when they mislead.

Since graduating I have worked to strengthen my quantitative side. I completed an online introductory course in statistics and have been working through R for Data Science by Hadley Wickham and colleagues, practising with the open datasets the Central Statistics Office publishes on population and housing. One small project involved joining census tables to public library locations to see which areas had the longest travel distances to a branch. Matching geographic boundaries across sources took longer than the analysis, which gave me a realistic sense of how much data work is preparation.

The library job itself has shaped my interests more than I expected. I often help people who are uneasy with online systems, and I have seen how a digital form that looks simple to its designers can exclude someone who does not have an email address or a reliable phone. It has made me think about who appears in administrative data and who is missing from it, a question I would like to examine more rigorously. Reading Weapons of Math Destruction by Cathy O'Neil sharpened this, particularly her argument that models built on proxy measures can reinforce the inequalities they are meant to describe.

Outside work, I keep the fixtures and availability spreadsheet for a Sunday football team, which mainly involves chasing replies on a group chat and redesigning the sheet so people actually fill it in. I am also learning Irish at a weekly evening class, slowly, and enjoy the discipline of practising something I am not naturally good at.

I am ready for postgraduate study because I know where my gaps are: I need stronger training in statistical modelling, programming and computational methods for text and networks, and the chance to apply them to real social questions under proper supervision. I bring a sociologist's habit of asking what a category means, experience of careful coding, and daily contact with the public services that much social data comes from. I would hope to use the degree to work in public sector or policy research, helping organisations count things in ways that reflect people's real circumstances.