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.
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 2019/2020:
Probability and Statistics for Network Analysis
Stochastic Models in Mathematical Genetics
Advanced Simulation Methods
Advanced Topics in Statistical Machine Learning
Algorithmic Foundations of Learning
Academic Year 2019/2020
- MSc and Diploma Handbook 2019/2020 [PDF]
- Timetable – Michaelmas Term 2019 [PDF]
- Practical timetable [PDF]
- Link to Canvas login for MSc in Statistical Science course site.
Other useful information
- Complaints and academic appeals within the Department of Statistics [PDF]
- Gutierrez Toscano Prize
- Graduate Liaison Group