MSc in Statistics (Research)

Page contents > Footnotes

Programme Code: TMSTRE

Department: Statistics

For students starting this programme of study in 2026/27

Guidelines for interpreting programme regulations

Classification scheme for the award of a taught master's degree (four units)
Exam sub-board local rules

Full-year programme. Students must take courses to the value of three full units and a dissertation.

Please note that places are limited on some optional courses. Admission onto any particular course is not guaranteed and may be subject to timetabling constraints and/or students meeting specific prerequisite requirements.
	Course code, title (unit value)

Paper 1	ST460 Statistical Inference: Principles and Methods (0.5) #

Paper 2	ST499 Dissertation (1.0)

Papers 3 & 4	Courses to the value of 1.0 unit(s) from the following:
	ST405 Unsupervised Machine Learning and Multivariate Data Analysis (0.5)
	ST411 Generalised Linear Modelling and Survival Analysis (0.5) #
	ST416 Multilevel Modelling (0.5) # (suspended 2026/27)
	ST418 Advanced Time Series Analysis (0.5) #
	ST442 Longitudinal Data Analysis (0.5) #
	ST444 Computational Data Science (0.5) #
	ST451 Bayesian Machine Learning (0.5) #

Papers 5-8	A
	ST405 Unsupervised Machine Learning and Multivariate Data Analysis (0.5)
	ST409 Stochastic Processes (0.5) #
	ST411 Generalised Linear Modelling and Survival Analysis (0.5) #
	ST416 Multilevel Modelling (0.5) # (suspended 2026/27)
	ST418 Advanced Time Series Analysis (0.5) #
	ST442 Longitudinal Data Analysis (0.5) #
	ST443 Machine Learning and Data Mining (0.5) #
	ST444 Computational Data Science (0.5) #
	ST445 Managing and Visualising Data (0.5) #
	ST446 Distributed Computing for Big Data (0.5) #
	ST449 Artificial Intelligence (0.5) #
	ST451 Bayesian Machine Learning (0.5) #
	ST454 Bayesian Data Analysis (0.5) # (suspended 2026/27)
	ST455 Reinforcement Learning (0.5) #
	ST456 Deep Learning (0.5) #
	ST457 Graph Data Analytics and Representation Learning (0.5) #
	ST459 Quantum Computation and Information (0.5) #
	ST463 Stochastic Simulation, Training, and Calibration (0.5) #
	EC402 Econometrics (1.0) #
	MA407 Algorithms and Computation (0.5) #
	MA427 Nonlinear Optimisation and Applications (0.5) #
	MY400 Fundamentals of Social Science Research Design (0.5)
	MY456 Survey Methodology (0.5) #
	MY457 Causal Inference for Observational and Experimental Studies (0.5) #
	MY459 Computational Text Analysis and Large Language Models (0.5) #
	MY461 Social Network Analysis (0.5) (suspended 2026/27)
	MY466 Population Analysis: Methods and Models (0.5) #
	Students can take up to a maximum of 1.0 unit(s) from the following courses: ST443, ST445, ST446, ST449, ST455, ST456, ST457.
	Other courses may be taken with permission, except for: ST429, ST436, ST440, MA415, MA416, MA420 and any courses indexed FM.

Footnotes

# means there may be prerequisites for this course. Please view the course guide for more information.

A: Papers 5-8, EC402: Statistics students taking EC402 will be required to register in late August in order to attend the econometrics component of the introductory course EC400. Students must pass an exam taken at the end of the introductory course in order to proceed to EC402.

This programme is externally accredited by the RSS. Further information is available on the Department of Statistics website.

Note for prospective students:
For changes to graduate course and programme information for the next academic session, please see the graduate summary page for prospective students. Changes to course and programme information for future academic sessions can be found on the graduate summary page for future students.

MSc in Statistics (Research)

Course code, title (unit value)

Footnotes