Semester 1 Review: MSc Operational Research
That’s a wrap for semester 1 of my MSc Operational Research with Data Science programme. I sat the last of my four exams yesterday afternoon; what better time to reflect on the previous 14 weeks.
Why an MSc in Operational Research?
It’s been 4 years since I graduated from the University of Warwick with a BSc in Mathematics. I completed a 2-year rotational grad scheme at Dunelm, the UK’s leading homeware retailer, and then went on to be a founding member of its Supply Chain Analytics team. To widen my breadth of experiences, I moved to Specsavers, the international chain of opticians, working in their global marketing analytics team.
I was motivated to study for an MSc in OR to develop my modelling and programming skills and build a deeper understanding of optimisation. My early career was full of business questions crying out for a mathematical approach – I want to continue sharpening my saw, ready for whatever I work on next.
I had wanted to return to formal study for a while, but only applied once the covid pandemic had subsided to a level at which in-person teaching was a safe bet. Whilst I’ve had good experience taking online courses before (in Python, SQL, PBI, Flask), I decided on the full-time MSc to ensure unilateral focus on my learning, and for the opportunities to network with bright individuals with the same priorities.
What is Operational Research?
The OR society gives this definition: ‘Operational Research (OR) is a scientific approach to the solutions of problems in the management of complex systems that enables decision makers to make better decisions.’
I like this definition for a couple of reasons: It’s broad enough to encapsulate the smorgasbord of methods, and it focuses on the application: we’re solving problems with a purpose to inform decisions, and not, as in some of pure maths, for problem’s sake!
What did my modules cover?
Running a barbershop, and want to know how the number of barbers/waiting area will impact the wait time for your customers (and maybe your tip?). I learnt to solve these problems analytical in Stochastic Modelling.
How could you set up the heating and ventilation in an office to change dynamically based on weather conditions and employee comfort? In Natural Computing, I programmed a 30-particle swarm to find optimal solutions in a multi-dimensional search space in Python.
Is your city council installing electric vehicle charging points? Where should they be placed to satisfy demand to minimise cost, and maximise user convenience? I worked in a group answering this question for the city of Dundee and presented our work to a stakeholder consultancy in our Methodology, Modelling and Consulting Skills module.
Using R, I used a Markov model to generate biblical passages, simulated the prisoner problem, and modelled the excess deaths over the covid period in the module Statistical Programming.
In core OR and Optimisation courses I opened the hood on methods used by linear solvers: exploring the simplex method, and on branch and bound methods for integer programming. Sensitivity analysis, and a little game theory were also in the curriculum.
Looking ahead
I’m yet to choose my modules for next semester, that’s my ‘homework’ for the Christmas hols! Scanning down the list, Operational Research in the Energy Industry, Reinforcement Learning and Cognitive Neuroscience have caught my eye. I’m also enrolled on an Italian course for extra credit. Dissertation topics will soon be on the agenda too.
When in Scotland…
Away from my studies, I’ve joined the Hillwalking club on a few weekends away, to Ullapool, Crainlarich, and to Loch Lomond. I’ve climbed my first few Munro’s and have had a fantastic time in the outdoors. Edinburgh is a beautiful place to live, with a backdrop of Arthur’s Seat, Edinburgh Castle, and Portobello beach available within a short run around the city.
Until next time,
Scott
