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Study information

Statistical Computing - 2024 entry

MODULE TITLEStatistical Computing CREDIT VALUE15
MODULE CODEMTH3045 MODULE CONVENERDr Ben Youngman (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11
Number of Students Taking Module (anticipated) 45
DESCRIPTION - summary of the module content

When we want to fit a statistical model to some data it is almost inevitable that a computer will make this process much easier. Computers can speed up calculations, avoid the tedium or potential for error of doing calculations by hand, and have allowed us to analyse amounts of data and fit new models that were simply impractical without them. Data Science is built on the fitting of statistical models to data. While such models continue to evolve, we must balance what's theoretically and practically possible; otherwise we have data that we can't analyse and models that we can't estimate. We can achieve more by fitting statistical models efficiently.

 

To efficiently fit statistical models, we often use fundamental mathematical concepts, including some that you will have previously seen, such as matrix decompositions. You will learn a variety of these concepts from the theory behind them to their role in analysing real-life data. You will see some important statistical models that rely on these concepts and how the R programming language can be used for computation, in particular some of its more advanced features for calculations and analysing data. You will gain experience in programming while learning new statistical methods and models, through interesting examples and exercises. After this module you will be able to analyse more complex data with more advanced statistical techniques.

 

MTH2006 or equivalent is a prerequisite for this module. You may find MTH3041 and MTH3028 helpful and/or of interest.

 

AIMS - intentions of the module

This module aims to help you develop advanced computational and mathematical skills for the statistical analysis of data, which are essential for advanced Data Science. The module will introduce important mathematical concepts and their place in efficiently fitting statistical models or contributing to new statistical models that let us better analyse data. Such skills and models are important for statistical research and for jobs that heavily involve statistical analysis, such as a Data Scientist.

 

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

 Module Specific Skills and Knowledge:

1. demonstrate an understanding of important mathematical concepts, such as matrix decompositions and optimisation algorithms, and their role in fitting statistical models;

2. apply these concepts to the fitting of various statistical models to data;

3. employ these concepts and fit statistical models to data using the programming language R;

4. demonstrate and compare fitting approaches for computational efficiency;

 Discipline Specific Skills and Knowledge

5. demonstrate an understanding of using computers to fit statistical models to data;

6. progress to study a wider range of computational tools and/or statistical methodology;

 Personal and Key Transferable / Employment Skills and Knowledge:

7. demonstrate an understanding of key computational aspects when fitting statistical models for the advanced study, application and development of statistical and data science.

 

SYLLABUS PLAN - summary of the structure and academic content of the module

Background in statistical computing: How computers perform calculations, efficiency in computation; computing with programming language R; compiled computer code; debugging, benchmarking and profiling.

Matrix-based computing: Fundamentals of matrices and matrix-based calculations; systems of linear equations; matrix decompositions; statistical applications, e.g. principal components, multivariate normal calculations.

Optimisation: One-dimensional optimisation; multi-dimensional optimisation, including variants of Newton's method; global optimisation; statistical applications, e.g. non-linear model fitting and uncertainty estimation.

Numerical calculus: Numerical and symbolic differentiation; Monte Carlo integration, quadrature, Laplace's method; statistical applications, e.g. the integrated Laplace approximation.

Advanced numerical and statistical methods: E.g. approximate Bayesian computation; discrete and fast Fourier transforms.


 

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 31 Guided Independent Study 119 Placement / Study Abroad
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning and teaching activities 31 Lectures/example classes
Guided independent study 119 Guided independent study
     

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework - example sheets 5 exercise sheets All Tutorial sessions during lectures/office hours, written feedback on work
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 0 Written Exams 50 Practical Exams 50
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework 1 50 12 pages All Written
Practical exam 50 3 hours (Summer) All Written

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Practical exam * Examination (3 hours) (50%) All August Ref/Def period
Coursework 1 and/or 2 * Reassessment coursework (50%)  All August Ref/Def period
       

*Please refer to reassessment notes for details on deferral vs Referral reassessment 

RE-ASSESSMENT NOTES

Deferrals: Reassessment will be by coursework and/or written exam in the deferred element only. For deferred candidates, the module mark will be uncapped. 

Referrals: Reassessment will be by a single exam worth 100% of the module only. As it is a referral, the mark will be capped at 40%.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

ELE - http://vle.exeter.ac.uk

 

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
Set Press, W.H., Flannery, B.P., Teukolsky, S.A. & Vetterling, W.T Numerical Recipes: the Art of Scientific Computing 3rd edition Cambridge University Press 2007 13: 9780521880688
Set Wickham, H. Advanced R Chapman and Hall 2014 978-1466586963
Set Wood, S N Core Statistics 2 Cambridge University Press 2015
Set Monahan, J F Numerical Methods of Statistics 2 Cambridge University Press
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES MTH2006
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 6 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 12th March 2024 LAST REVISION DATE Tuesday 12th March 2024
KEY WORDS SEARCH Numerical optimisation; matrix computations; statistical models

Please note that all modules are subject to change, please get in touch if you have any questions about this module.