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MSc in Statistics

Page contents > Prerequisite Requirements and Mutually Exclusive Options

Programme Code: TMST

Department: Statistics

For students starting this programme of study in 2021/22

Guidelines for interpreting programme regulations


Academic-year programme. Students must take courses to the value of four full units.

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.

Paper

Course number, title (unit value)

Paper 1

ST425 Statistical Inference: Principles, Methods and Computation (1.0) #

Papers 2 & 3

Courses to the value of 2.0 unit(s) from the following:

 

ST405 Multivariate Methods (0.5) #

 

ST416 Multilevel Modelling (0.5) #

 

ST418 Non-Linear Dynamics and the Analysis of Real Time Series (0.5) #  (not available 2021/22)

 

ST422 Time Series (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 Applied spatio-temporal analysis (0.5) #

 

ST455 Reinforcement Learning (0.5) # *

 

ST456 Deep Learning (0.5) # *

 

MY459 Special Topics in Quantitative Analysis: Quantitative Text Analysis (0.5) #

 

MY461 Social Network Analysis (0.5)

Paper 4

Courses to the value of 1.0 unit from the following:

 

ST405 Multivariate Methods (0.5) #

 

ST409 Stochastic Processes (0.5) #

 

ST411 Generalised Linear Modelling and Survival Analysis (0.5) #

 

ST416 Multilevel Modelling (0.5) #

 

ST418 Non-Linear Dynamics and the Analysis of Real Time Series (0.5) #  (not available 2021/22)

 

ST422 Time Series (0.5) #

 

ST426 Applied Stochastic Processes (0.5)  (not available 2021/22)

 

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 Applied spatio-temporal analysis (0.5) #

 

ST455 Reinforcement Learning (0.5) # *

 

ST456 Deep Learning (0.5) # *

 

MA407 Algorithms and Computation (0.5) #

 

MA427 Mathematical Optimisation (0.5) #

 

MY456 Survey Methodology (0.5) #

 

MY457 Causal Inference for Observational and Experimental Studies (0.5) #

 

MY459 Special Topics in Quantitative Analysis: Quantitative Text Analysis (0.5) #

 

MY461 Social Network Analysis (0.5)

 

Other courses may be taken with permission, except for: ST429, ST433, ST436, ST439, ST440, MA415, MA416, MA420 and any courses indexed FM.

Prerequisite Requirements and Mutually Exclusive Options

* means available with permission

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

The total value of all non-ST courses should not exceed one unit.

Students can take up to a maximum of 1.0 unit from the following courses: ST443, ST444, ST445, ST446, ST449, ST451, ST455, ST456.

This programme is externally accredited by the RSS. Further information is available on the 

The  facilitates comparability and compatibility between higher education systems across the European Higher Education Area. Some of the School's taught master's programmes are nine or ten months in duration. If you wish to proceed from these programmes to higher study in EHEA countries other than the UK, you should be aware that their recognition for such purposes is not guaranteed, due to the way in which ECTS credits are calculated.

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

 
 
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