Scientific computing personal statement example

Why do you want to study this course or subject?

For several years my job involved recording flow rates, turbidity and dosing levels at a water treatment works, then typing them into a spreadsheet. The spreadsheet could tell us what had happened, but it could not tell us what would happen if we changed a setting, and nobody on shift could answer that either. I started reading about how plant behaviour is modelled and found that the interesting part was not the chemistry I already half knew, but the mathematics of describing a changing system and then getting a computer to solve it approximately, because the exact answer is usually unavailable. That is the work I want to do. Since then I have taught myself enough Python to write a small simulation of a stirred tank with a step change in inlet concentration, solving the governing equation with Euler's method and then with a fourth-order Runge-Kutta routine, and comparing both against the analytical solution. Watching the error shrink as I reduced the step size, and then seeing the runtime grow, made the trade-offs concrete in a way that reading about them had not. I am drawn to scientific computing specifically because it sits between the mathematics and the application. I want to understand why a method converges, how floating-point arithmetic quietly loses precision, and how to choose an approach that is good enough for the question being asked. A degree gives me the linear algebra, analysis and programming depth I cannot reach by myself on a night shift, and I am ready to spend three or four years on it properly rather than continuing to pick things up in fragments.

How have your qualifications and studies helped you to prepare?

I left school with GCSEs including grade B in mathematics and went straight into work, so returning to study meant starting again with the fundamentals. I am currently finishing an Access to Higher Education Diploma in Science at my local college, studying mathematics, physics and chemistry over two evenings a week and one day release. The mathematics units have been the most useful preparation: differentiation and integration, sequences and series, matrices, and an introduction to differential equations. I found sequences and limits unexpectedly satisfying, because they underpin the iterative methods I had been using without really understanding why they settle on an answer. For my physics investigation I modelled projectile motion with air resistance, which cannot be solved neatly by hand, so I wrote a short numerical integration in Python and checked it against the drag-free case as a sanity test. My tutor's main comment was that I should state my assumptions more clearly, which was fair and has changed how I write up work. Alongside the diploma I have worked through much of a university-level introductory text on numerical methods, covering root finding, interpolation and Gaussian elimination, and I have been using an online linear algebra course to get more comfortable with eigenvalues and vector spaces. Studying while working shifts has made me organised out of necessary habit: I plan a week at a time and accept that some evenings are for revision rather than new material.

What else have you done to prepare outside of education, and why are these experiences useful?

My work at the treatment works gave me habits that transfer well. Readings had to be recorded consistently and honestly, because a figure entered carelessly at three in the morning becomes somebody else's confusing data later. I was often the person who noticed a sensor drifting because the numbers looked wrong before any alarm triggered, and I learned to write handover notes that a colleague could act on without ringing me. I also spent a year helping to move our paper log sheets onto a shared digital form, which mostly meant sitting with operators to find out which fields they actually used. That taught me that a tool nobody understands will simply be worked around. For the past eighteen months I have volunteered at a community centre coding club, helping teenagers and a few adults with beginner Python. Explaining why a loop runs one time too many, or why comparing decimal numbers for exact equality is a bad idea, has forced me to be precise about things I thought I knew. In my own projects I keep a repository of small numerical experiments: a Monte Carlo estimate of an area, a routine that fits a straight line to noisy data by least squares, and a comparison of matrix multiplication written in plain loops against a library version, which was a memorable lesson in why libraries exist. Outside study I play five-a-side and have been learning to repair bicycles, which suits the same instinct for taking something apart to find out how it actually works.

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