Study information

Advanced Topics in Statistics - 2024 entry

MODULE TITLEAdvanced Topics in Statistics CREDIT VALUE15
MODULE CODEMTHM017 MODULE CONVENERDr Dorottya Fekete (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 0 (Oct start) 0 (Jan start) 5 (Oct start) 0 (Jan start) 0 (Oct start) 5 (Jan start)
Number of Students Taking Module (anticipated) 100
DESCRIPTION - summary of the module content

This module offers an insight to cutting-edge statistical learning techniques that are at the forefront of current research and application. You will have opportunity to explore a range of topics including time series modelling and forecasting, decision trees, random forests, support vector machines, neural networks and Bayesian computation. The choice of topics in any year may change to ensure that the content of the module reflects the rapid change in this exciting area.

AIMS - intentions of the module
The aims are to expose the student to some recent developments in statistics; to allow the student to study one or more advanced topics in some depth.
 
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 current developments in statistics;
 
2 Demonstrate an understanding of the strengths and limitations of different statistical approaches;
 
3 Demonstrate the ability to apply advanced statistical methodology across a variety of settings;
 
Discipline Specific Skills and Knowledge:
 
4 Demonstrate an understanding of advanced regression modelling;
 
5 Demonstrate an understanding of modelling data with dependence;
 
6 Demonstrate the ability to self-learn further details of the methodology introduced within topics;
 
Personal and Key Transferable/ Employment Skills and Knowledge:
 
7 Statistical analysis skills;
 
8 Self-learning and making effective use of learning resources;
 
9 Effective use of learning resources;
 
10 Report writing and presentation.
 
SYLLABUS PLAN - summary of the structure and academic content of the module
The syllabus will depend upon the module topic(s) offered and will be specified in detail by the lecturer(s) and agreed by the module coordinator for any particular year. Examples of topics include time series modelling and forecasting, clustering, neural networks and Bayesian computation. 
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 30 Guided Independent Study 120 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching Activities 20 Lectures
Scheduled Learning and Teaching Activities 10 Problem-solving sessions
Guided Independent Study 56 Self-study & background reading
Guided Independent Study 64 Coursework

 

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
Feedback on unassessed problem sheets and data analyses 24 All Oral

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework 100 Max 10 pages (plus appendices) All Oral

 

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
Coursework Coursework (100%) All August Ref/Def Period

 

RE-ASSESSMENT NOTES

Reassessment will be by resubmission of the coursework element only. For referred candidates, the mark will be capped at 50%. For deferred candidates, the mark will be uncapped.

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

Basic reading:

 

ELE: http://vle.exeter.ac.uk

 

Web based and Electronic Resources:

 

Other Resources:

 

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
Set Faraway, J.J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models Chapman & Hall 2006 158488424X
Set Wakefield, J. Bayesian and Frequentist Regression Methods Springer 2013 978-1441909244
Set Venables, W.N., Ripley, B.D. Modern Applied Statistics with S 2nd Springer 2003 978-0387954578
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES MTHM501, MTHM502, MTHM503
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 12th March 2024 LAST REVISION DATE Wednesday 23rd October 2024
KEY WORDS SEARCH None Defined

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