ECMM459 | 2021/2 | University of Exeter

MODULE TITLE	Statistical Modelling	CREDIT VALUE	15
MODULE CODE	ECMM459	MODULE CONVENER	Dr Tinkle Chugh (Coordinator)

DURATION: TERM	1	2	3
DURATION: WEEKS		11

Number of Students Taking Module (anticipated)	90

DESCRIPTION - summary of the module content

*** This module is a “professional” module intended to be taught in a short-fat format based around 3-day teaching blocks, as part of the MSc Data Science (Professional) programme. ***

In this course we look at the concepts and methods of modern statistics in greater detail. The course will cover various topics in statistical modelling with Bayesian flavor, including generalised linear models, Hierarchical statistical models, Generative and Discriminative models, Hidden Markov models, use of Markov Chain Monte Carlo and Gaussian processes. The module will include practical application of these techniques as well as theoretical underpinnings and model choice.

Pre-requisites: ECMM456 Fundamentals of Data Science (Professional)

Co-requisites: None.

AIMS - intentions of the module

The aim of this module is to introduce you to modern methods in statistics, both conceptually and computationally.

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 a sound understanding of the reasoning behind choice of methods in statistical modelling.

2. Apply a range of statistical modelling techniques to real-life situations and datasets.

3.Perform data analyses by understanding the underlying principles behind different methods.

Discipline Specific Skills and Knowledge

4. Show sufficient knowledge of modern statistical methods both conceptual and computational.

Personal and Key Transferable / Employment Skills and Knowledge

5. Reason using abstract ideas, formulate and solve problems and communicate reasoning and solutions effectively in writing.

6. Use learning resources appropriately.

7. Exhibit self management and time management skills.

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

Topics will include:

• Basics of Bayesian statistical modelling

• Generalised Linear Models

• Markov Chain Monte Carlo

• Generative and discriminative models

• Hierarchical statistical modelling

• Hidden Markov models

• Introduction to Gaussian Processes

LEARNING AND TEACHING

LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)

Scheduled Learning & Teaching Activities	30	Guided Independent Study	46	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	Workshop/Practical classes in a computer lab
Guided independent study	46	Coursework preparation and self-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
Exercise/Quiz	1h x 4	All	Written

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 report	60	2000-3000 words	All	Written
Quiz	40	2-3 hours	All	Written/ELE

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 report	Coursework report	All	Within 8 weeks

RE-ASSESSMENT NOTES

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.

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

Reading list for this module:

Type	Author	Title	Edition	Publisher	Year	ISBN
Set	Gelman, A., Carlin, J., Stern, H., Dunson, D., Vehtari, A. and Rubin, D.	Bayesian data analysis	3rd	CRC	2008
Set	Gamerman, D. and Lopes H. F.	Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference		CRC Press	2006
Set	Banerjee, S., Bradley, P. Carlin, A.& Gelfand, E.	Hierarchical Modeling and Analysis for Spatial Data		CRC Press	2014
Set	Donovan, Therese and Mickey, Ruth M.	Bayesian Statistics for Beginners: a step-by-step approach		OUP Oxford	2019	9780198841296
Set	Carl Edward Rasmussen, Christopher K. I. Williams	Gaussian Processes for Machine Learning		MIT Press	2006	978-0262182539
Set	Murphy, K.	Machine Learning: A Probabilistic Perspective	1st	MIT Press	2012	978-0-262-018029

CREDIT VALUE	15	ECTS VALUE	7.5

PRE-REQUISITE MODULES	ECMM456
CO-REQUISITE MODULES

NQF LEVEL (FHEQ)	7	AVAILABLE AS DISTANCE LEARNING	No
ORIGIN DATE	Monday 5th August 2019	LAST REVISION DATE	Monday 21st February 2022

KEY WORDS SEARCH	Statistical Modelling

Statistical Modelling - 2021 entry