Advanced Computer Science (Data Analytics) MSc postgraduate personal statement example

Personal statement example

Every Thursday evening at the community pharmacy where I work as a stock assistant, I count the items due to expire within ninety days. For my first few months I simply wrote them on a clipboard. Then I noticed that the same handful of products, mostly seasonal hay fever tablets and travel sickness remedies, appeared on the list every year, and that our reorder quantities came from a spreadsheet nobody had revisited since it was built. With the manager's permission I exported eighteen months of sales and wastage figures and plotted weekly demand for those lines. The pattern was obvious once drawn: we were ordering at a flat rate for products with sharp seasonal peaks. I proposed a simple adjustment based on the previous year's weekly sales, and over the following spring the number of hay fever packs written off fell noticeably. It was a modest change, but it showed me how much routine data sits unused in ordinary workplaces, and how little sophistication it can take to make it useful.

That experience is why I want to move from applied curiosity to rigorous training in data analytics. My undergraduate degree was in Economics, with optional programming units in which I learned Python and SQL and took a course on statistical methods covering regression, hypothesis testing and an introduction to time series. My dissertation examined bus punctuality in my home city using publicly available timetable and real-time arrival data. Collecting the data was the hardest part: I wrote a script that queried an open feed every few minutes for six weeks, then spent a long time cleaning duplicated records and journeys that vanished mid-route. I modelled lateness against time of day, weather and route length using linear regression, and found that delays accumulated most on routes crossing the city centre during the school run. My supervisor rightly pointed out that my model treated observations as independent when consecutive stops on one journey clearly were not, and I finished the project aware of what I did not yet know how to handle properly. I want to learn the methods that deal with structure like that, from mixed models to more modern machine learning approaches, and to understand when each is appropriate.

I have read beyond my coursework to prepare. Working through Hastie, Tibshirani and Friedman's The Elements of Statistical Learning has been slow, but it clarified the bias-variance trade-off in a way my lecture notes had not, and I reworked my bus model with regularised regression as practice. I have also become more interested in the engineering side of analytics: how data is stored, queried efficiently and kept reliable once a project grows beyond a single spreadsheet. A postgraduate course in computer science, rather than statistics alone, appeals to me because I want to build dependable systems as well as fit models.

Outside work I volunteer as scorer for a Sunday cricket league. Keeping the book by hand, and later entering it into the league's online system, has taught me patience with detail and an appreciation of how one wrongly recorded ball changes every figure downstream. It is also, honestly, just an enjoyable way to spend a summer afternoon.

I bring a quantitative background, practical experience of messy real-world data, and the habit of asking whether a routine process could be done better. What I lack is depth in algorithms, data management and advanced machine learning, and that is precisely what I hope to gain. In the longer term I would like to work in public sector or health analytics, where careful use of data can make everyday services work more smoothly for the people relying on them.