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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 usually take four or five courses each term. Most courses are sixteen lectures or the equivalent, plus four classes going through set problem questions.

There are five core courses (three taken in the first term, two in the second term). Some of these courses include practical sessions, which students must attend.

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).
Statistical Machine Learning

Options

Options will vary from year to year but are expected to include in 2021/2022:

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

Algorithmic Foundations of Learning

Academic Year 2021/2022

All the course information for the MSc/PGDip in Statistical Science can be found on the MSc/PGDip in Statistical Science Canvas site (single sign-on required).

  • Gutierrez Toscano Prize is given for the best performance in the MSc in Statistical Science.