MSc and PG Diploma in Applied Statistics

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Academic Year 2016/2017

Our new delivery and content

We have made some changes to the content and delivery of the MSc in Applied Statistics. The new programme will be delivered for the first time in 2016-2017. We have more material on Statistical Machine Learning and Computational Statistics and the number of lectures in options courses is set to increase as we open up our third and fourth year undergraduate courses to MSc students. The majority of lectures in the new MSc will be 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).
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).
Data Mining and Machine Learning (Unsupervised Machine Learning; Kernel and Ensemble methods).


Options will vary from year to year. In 2016-2017 the first term options are:
Probability and Statistics for Networks
Graphical Models
Stochastic Models in Mathematical Genetics.

We expect the second term options to be:
Statistical Machine Learning
Actuarial Science
Advanced Simulation Methods

Academic Year 2016/2017

  • MSc and Diploma Handbook 2016/2017 [PDF]
  • Timetable - Michaelmas Term 2016 [PDF]
  • Link to Weblearn login for MSc in Applied Statistics course site.

Other useful information

Skills and study resources