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MSc in Statistical Science

The majority of lectures in the MSc in Statistical Science are delivered in courses alongside senior undergraduates.

Students take four or exceptionally five courses each term. All courses are sixteen lectures. Three courses each term are core courses, and students must complete the practical sessions in these courses.

Core courses

Applied Statistics (theory and application of linear and mixed models and generalised linear models).
Foundations of Statistical Inference (Bayes & Frequentist estimation; Decision theory; Variational Methods and EM).
Statistical Programming (MSc only – R programming; Graphics and visualisation; Advanced R).
Computational Statistics (Non-linear and non-parametric models; Bootstrap; Hidden Markov Model).
Bayes Methods (Prior elicitation; Bayesian non-parametrics; Approximation methods; Case studies).
Statistical Machine Learning


Options will vary from year to year but are expected to include in 2018/2019:

Probability and Statistics for Network Analysis
Graphical Models
Stochastic Models in Mathematical Genetics
Actuarial Science
Advanced Simulation Methods
Advanced Topics in Statistical Machine Learning

Algorithmic Foundations of Learning
Topics in Computational Biology

Academic Year 2018/2019

  • MSc and Diploma Handbook 2018/2019 [PDF]
  • Timetable – Michaelmas Term 2018 [PDF]
  • Link to Weblearn login for MSc in Statistical Science course site. All course material can be found here.

Other useful information

Skills and study resources