Mathematics MSc Programmes

Study Programme

Information about the Statistics and OR study programme.

The study programme is divided into three parts.

  • Part 1 covers the core skills of Statistics and Operational Research. The majority of the core courses are in Semester 1 (S1) from September to December.
  • Part 2 gives students the opportunity to tailor their degree by selecting from a broad range of optional courses. These are taught mainly in Semester 2 (S2) from January to March.
  • Part 3 begins in June and comprises a three month project on which students base their dissertations.

The length of the programme and its courses are measured in points. The whole programme is 180 points, comprising 120 points of core and optional courses in Parts 1 and 2, and the 60 point dissertation in Part 3.

The demands and rewards of studying for an OR MSc are well captured in the following quotation from student feedback:

The year has been very, very intense and I'll walk away from this with a skill set that I would never have imagined possible from just one year!

For 2017-18 the provisional course structure is as follows:

Provisional compulsory courses for 2017-18

  • Fundamentals of Operational Research (10 credits, Semester 1)
  • Fundamentals of Optimization (10 credits, Semester 1)
  • Methodology, Modelling and Consulting Skills (10 credits, Semester 1)
  • Simulation (10 credits, Semester 1)
  • Bayesian Theory (10 credits, Semester 1)
  • Statistical Programming (10 credits, Semester 1)
  • Generalised Regression Models (10 credits, Semester 2)
  • Statistical Research Skills (10 credits, Semester 2)

Provisional optional courses for 2017-18

  • Statistical Consultancy (10 credits, Semester 1)
  • Statistical Methodology (10 credits, Semester 1)
  • The Analysis of Survival Data (10 credits, Semester 1)
  • Incomplete Data Analysis (10 credits, Semester 1)
  • Scientific Computing (10 credits, Semester 1)
  • Python Programming (10 credits, Semester 1)
  • Theory of Statistical Inference (10 credits, Semester 2)
  • Multivariate Data Analysis (10 credits, Semester 2)
  • Stochastic Modelling (10 credits, Semester 2)
  • Time Series (10 credits, Semester 2)
  • Large Scale Optimization for Data Science (10 credits, Semester 2)
  • Credit Scoring (10 credits, Semester 2)
  • Biomedical Data Science (10 credits, Semester 2)
  • Bayesian Data Analysis (10 credits, Semester 2)
  • Topics in Applied Operational Research (10 credits, Semester 2)
  • Nonparametric Regression Models (10 credits, Semester 2)
  • Risk and Logistics  (10 credits, Semester 2)
  • Integer and Combinatorial Optimization  (10 credits, Semester 2)
  • Operational Research in the Energy Industry  (10 credits, Semester 2)
  • Topics in Applied Optimization  (10 credits, Semester 2)
  • Genetic Epidemiology (10 credits, Semester 2)
  • Machine Learning Practical (20 credits, Full Year)