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MODULE TITLE	Data Science and Statistical Modelling in Space and Time	CREDIT VALUE	15
MODULE CODE	MTHM505	MODULE CONVENER	Dr James Salter (Coordinator)

DURATION: TERM	1	2	3
DURATION: WEEKS		6 (October start) / 0 (January start)	0 (October start) / 5 (January start)

Number of Students Taking Module (anticipated)	50

DESCRIPTION - summary of the module content

In this module you will learn about modelling data that is collected over space and time. Advances in statistical and computing methodology together with the increasing availability of data recorded at very high spatial and temporal resolution has led to great advances in temporal, spatial and, more recently, spatio–temporal methods. In this module you will have the opportunity to explore the theoretical aspects of these methods and will learn how to implement them to explore and understand patterns in space and time within data from real-world examples.

AIMS - intentions of the module

In many applications of Data Science and Statistics, data are measured, or collected, over space and time. In such cases, methods that assume that data are independent may not be suitable, and more sophisticated modelling approaches are required. The aim of this model is to introduce the concepts behind dependencies in time and space and to learn methods for time series modelling and spatial analyses. The module will cover both theoretical and practical aspects of modelling data over space and time, with examples from computer modelling, the environment and health.

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. Model correlated data structures in continuous space and time co-ordinates;
2. Explain what a Gaussian process is and how it can be used to model spatially correlated data in 1, 2 or many dimensions;

3. Describe the difference between space and time in modelling and create models using both ARIMA and state space modelling of time series;

4. Demonstrate an understanding of spatio-temporal modelling;

5. Use appropriate software and a suitable computer language for modelling correlated data in space, time and both together;

Discipline Specific Skills and Knowledge

6. Apply the theory of statistical modelling of spatially and temporally correlated data and analyse the resulting models;

Personal and Key Transferable / Employment Skills and Knowledge

7. Utilise advanced data analysis skills and be able to communicate associated reasoning and interpretations effectively in writing;
8. Use relevant computer software competently;

9. Utilise learning resources appropriately.

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

- Dependent data; distance and correlation, stationarity, the Gaussian process; covariance functions; nuggets, sampling from Gaussian processes;

- Types of covariance function, Bochner’s theorem; separability; fitting Gaussian processes; examples;

- Kriging; variograms and covariance functions; time and space; ARIMA models; state space models; dynamic linear models;

- Spatio-temporal models, hierarchical modelling.

LEARNING AND TEACHING

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

Scheduled Learning & Teaching Activities	30.00	Guided Independent Study	118.00	Placement / Study Abroad

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	Hands-on practical sessions
Guided Independent Learning	118	Coursework, background reading, preparation for contact time, preparation for assessments

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
Online quizzes	4 x 30 minutes	All	Electronic

SUMMATIVE ASSESSMENT (% of credit)

Coursework	100	Written Exams	0	Practical Exams

DETAILS OF SUMMATIVE ASSESSMENT

Form of Assessment	% of Credit	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Coursework – practical modelling exercises and theoretical problems	80	20 Hours	All	Written and oral
Class test	20	2 Hours	All	Electronic, 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 – modelling exercises and theoretical problems	Coursework – modelling exercises and theoretical problems (80%)	All	August Ref/Def Period
Class Test	Class Test (20%)	All	August Ref/Def Period

RE-ASSESSMENT NOTES

Reassessment will be by coursework in the failed or deferred element only. For deferred candidates, the module mark will be uncapped. For referred candidates, the module mark 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	Search
Set	Cressie, N.	Statistics for Spatial Data		Wiley	1991	000-0-471-84336-9	[Library]
Set	Shumway, R H, Stoffer, D S	Time series analysis and its applications		Springer	2015	978-1-4419-7865-3	[Library]

CREDIT VALUE	15	ECTS VALUE

PRE-REQUISITE MODULES	MTHM501, MTHM502
CO-REQUISITE MODULES

NQF LEVEL (FHEQ)		AVAILABLE AS DISTANCE LEARNING	No
ORIGIN DATE	Monday 14 September 2020	LAST REVISION DATE	Thursday 26 January 2023

KEY WORDS SEARCH	None Defined

MTHM505 - Data Science and Statistical Modelling in Space and Time (2023)