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MSc in Statistics (Financial Statistics) (Research)

Page contents > Prerequisite Requirements and Mutually Exclusive Options

Programme Code: TMSTFSRE

Department: Statistics

For students starting this programme of study in 2024/25

Guidelines for interpreting programme regulations

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

Academic-year programme. Students take three compulsory courses (two units), a dissertation, and optional courses to the value of one unit.

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) #

Paper 2

ST436 Financial Statistics (0.5) #

Paper 3

ST458 Financial Statistics II (0.5) #

Paper 4

ST499 Dissertation (1.0)

Paper 5

Courses to the value of 1.0 unit(s) from the following:

 

MA417 Computational Methods in Finance (0.5) #

 

ST405 Multivariate Methods (0.5) #

 

ST409 Stochastic Processes (0.5) #

 

ST411 Generalised Linear Modelling and Survival Analysis (0.5) #

 

ST416 Multilevel Modelling (0.5) #  (not available 2024/25)

 

ST418 Advanced Time Series Analysis (0.5) #

 

ST426 Applied Stochastic Processes (0.5)  (not available 2024/25)

 

ST429 Statistical Methods for Risk Management (0.5) #

 

ST439 Stochastics for Derivatives Modelling (0.5) #  (not available 2024/25)

 

ST440 Recent Developments in Finance and Insurance (0.5) #  (not available 2024/25)

 

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) #

 

ST448 Insurance Risk (0.5) #  (not available 2024/25)

 

ST449 Artificial Intelligence (0.5) #

 

ST451 Bayesian Machine Learning (0.5) #

 

ST454 Bayesian Data Analysis (0.5) #

 

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) #

 

FM402 Financial Risk Analysis (0.5) #

 

FM413 Fixed Income Markets (0.5) #

 

FM429 Asset Markets A (0.5) #

 

FM441 Derivatives (0.5) #

 

FM442 Quantitative Methods for Finance and Risk Analysis (0.5) #

 

MA407 Algorithms and Computation (0.5) #

 

MA415 The Mathematics of the Black and Scholes Theory (0.5) #

 

MA416 The Foundations of Interest Rate and Credit Risk Theory (0.5) #

 

MA427 Mathematical Optimisation (0.5) #

 

MA435 Machine Learning in Financial Mathematics (0.5) #

 

MY456 Survey Methodology (0.5) #

 

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

 

MY459 Quantitative Text Analysis (0.5) #

 

MY461 Social Network Analysis (0.5)

 

Or other non-ST course(s), with permission

Prerequisite Requirements and Mutually Exclusive Options

# 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.

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.

 
 
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