All 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). The information includes the handbook, timetables, examinations and assessments, and student support and feedback.
The majority of courses for the MSc in Statistical Science are partially deliverely alongside senior undergraduates (3rd and 4th year). Students will take the same lectures, but separate classes, which are for going through set problem questions. A small number of the core courses also have some accompanying practical sessions. Most courses are sixteen lectures, or the equivalent, plus four classes. Statistical Programming is an MSc only course, which has a split of lectures and practical sessions. There are five core courses (three in the first term and two in the second term). Options may vary from year to year.
- Applied Statistics (theory and application of linear and mixed models and generalised linear models.
- Foundations of Statistical Inference (Bayes and frequentist estimation, decision theory, variational methods and EM).
- Statistical Programming (R programming, graphics and visualisations, advanced R).
- Computational Statistics (non-linear and non-parametric models, bootstrap, hidden Markov model).
- Statistical Machine Learning (Unsupervised/Supervised Learning; Foundations of Statistical Learning; Generative/Discriminative methods; Optimisation for ML; Overview of different ML methods: Discriminant Analysis, Naive Bayes, Trees, Random Forests, Boosting, Neural Networks).
These are the optional courses expected in 2023-2024.
- Stochastic Models in Mathematical Genetics
- Probability and Statistics for Network Analysis
- Graphical Models
- Advanced Topics in Statistical Machine Learning
- Advanced Simulation Methods
- Bayes Methods
To see more detail on what the courses cover, please see the MSc handbook.