Data Analytics for Government

Credits

10 credit module (reference MAT032)

Dates

Autumn semester. Typically, Tuesday early afternoons and Thursday lunchtimes, starting early October*.

Cost

£610 (for the 2023-24 academic year)

Assessment

2 hour written examination (80%)

Coursework 20% (two pieces of written work)

Individual assignment (10%)

Individual case study (10%)

Outline description

In this module we cover the fundamentals of survey statistics. In particular:

• standard methods of drawing samples from finite populations
• how to make inferences about population characteristics
• survey-based estimation of population totals and related quantities
• regression estimation for modelling relationships between variables
• the principles and methods used to compensate for non-response following survey data collection
• calibration methods for household surveys
• index numbers.

Students who have only completed A-level statistics may have to complete some revision before starting.

Objectives

On successful completion of the module, you should be able to:

• estimate means, totals, proportions, and ratios of population variables from data collected using standard sampling methods
• adjust estimates to compensate for the effects of unit non-response
• use calibration to improve estimates from household surveys
• assess the suitability of a survey for a given estimation problem.

Skills you will practise and develop

• data analytics: collection, management and cleaning of data
• mathematical reasoning: construction of effective statistical indicators.

Syllabus content

• estimation for simple random sampling, stratified sampling and cluster sampling
• regression and ratio estimation
• non-response and imputation of missing values
• calibration
• index numbers.

Delivery

You will be guided through learning activities appropriate to your module, which may include:

• weekly face to face classes (e.g. labs, lectures, exercise classes)
• electronic resources that you work through at your own pace (e.g. videos, exercise sheets, lecture notes, e-books, quizzes).

You are also expected to undertake self-guided study throughout the duration of the module.

Credits

10 credit module (reference CMT314)

Dates

Autumn semester. Typically, Monday mornings, starting early October*.

Cost

£610 (for the 2023-24 academic year)

Assessment

A blend of assessment types which may include coursework and portfolio assessments, class tests, and/or formal examinations.

Outline description

• this module will introduce core data science concepts, including understanding of the different types of data sources available (administrative data, survey data, open data, big data, etc)
• how to collect data, including innovative data collection methods, e.g. web scraping
• understanding the challenges with unstructured data
• how to treat different data types; how to undertake basic data analysis (structured and unstructured data)
• how to present data through basic data visualisations.

Objectives

On successful completion of the module, you should be able to:

• use the Python programming language to complete a range of programming tasks
• critically analyse and discuss methods of data collection
• extract textual and numeric data from a range of sources, including online
• reflect upon the legal, ethical and social issues relating to data science and its applications.

Skills you will practise and develop

• fundamental programming in Python
• reading and writing common data formats
• data analysis using appropriate libraries.

Syllabus content

• basic programming in Python: Fundamental data types, program control structures, basic language features
• data extraction and importing; analysis using common libraries (e.g. Pandas, Numpy/Scipy)
• natural language processing using common libraries (e.g. NLTK, SpaCY)
• retrieving data from online sources (web scraping, APIs)
• data science applications
• legal issues relating to data science (GDPR)
• social and ethical issues relating to data science.

Delivery

Modules will be delivered through blended learning. You will be guided through learning activities appropriate to your module, which may include:

• online resources that you work through at your own pace (e.g. videos, web resources, e-books, quizzes)
• online interactive sessions to work with other students and staff (e.g. discussions, live streaming of presentations, live-coding, team meetings)
• face-to-face small group sessions (e.g. help classes, feedback sessions).

Credits

10 credit module (reference CMT315)

Dates

Spring semester. Typically, Tuesday mornings and Monday afternoons, starting late January*.

Cost

£610 (for the 2023-24 academic year)

Assessment

A blend of assessment types which may include coursework and portfolio assessments, class tests, and/or formal examinations.

Outline description

This will be a practical module, which will consider programming with structured and unstructured data and statistical analysis of this data. You will learn how to analyse both numeric and textual data using a range of computational programming languages.

Objectives

On successful completion of the module, you should be able to:

• use code to extract, store and analyse textual and numeric data
• carry out data analysis and statistical testing using code
• critically analyse and discuss methods of data collection, management and storage
• analyse and visualise textual and numeric data from a range of sources, including online.

Skills you will practise and develop

• data analysis using appropriate libraries.

Delivery

Modules will be delivered through blended learning. You will be guided through learning activities appropriate to your module, which may include:

• online resources that you work through at your own pace (eg videos, web resources, e-books, quizzes)
• online interactive sessions to work with other students and staff (eg discussions, live streaming of presentations, live-coding, team meetings)
• face-to-face small group sessions (eg help classes, feedback sessions).

Credits

10 credit module (reference SIT760)

Dates

Spring semester. Typically, Tuesday afternoons during the spring semester, starting early March*.

Cost

£610 (for the 2023-24 academic year)

Assessment

This short module will be assessed through one 2-hour examination during which students will answer two exam questions.

Outline description

This module provides an overview of issues and ideas concerning the scope and organisation of official statistics, as well as its processes and products.

The module provides a general foundation for the more detailed study of these elements and identifies links with other relevant disciplines.

Objectives

On successful completion of the module, you should be able to:

• critically evaluate the UK Statistical System and Code of Practice
• show clear understanding of Quality Control, Dissemination and Ethical issues relevant to the production and management of Official Statistics.

Skills you will practise and develop

You will have a broad overview of the fundamental issues underlying the organisation of official statistics and be able to apply this knowledge in discussing the relative merits of alternative approaches.

Mandatory topics

• values and trustworthiness
• overview of the importance of statistics, policy and administrative uses.
• history of the development of official statistics in the UK.
• statistical legislation
• quality
• ethics
• dissemination.

Delivery

The module will be delivered through a mix of large group and small group sessions, as well as online teaching and learning activities and materials.

Credits

20 credit module (reference CMT220)

Dates

Spring semester. Typically, Wednesday mornings, starting late January*.

Cost

£1,220 (for the 2023-24 academic year)

Assessment

A blend of assessment types which may include coursework and portfolio assessments, class tests, and/or formal examinations.

Outline description

Database systems are the most widely used software systems in commerce and industry. Database management systems are used to store and manage the complex integrated information resources of organisations. This module introduces the theoretical and practical issues relating to the design and use of these systems. In addition to the provision of a sound foundation in traditional, second generation database systems, it explores the representation and management of complex information resources with NoSQL database technology.

Objectives

On successful completion of the module, you should be able to:

• design a relational database, i.e. map conceptual models to efficient representations in a database schema
• manage relational database systems
• use SQL to define and query a relational database
• discuss and evaluate the principles of data integrity, security and concurrency control
• model and manage information using markup languages
• describe and evaluate the principles behind other types of database management systems, for example NoSQL.

Skills you will practise and develop

• understanding the role of information in decision making
• designing relational databases (including conceptual design, logical design, physical design)
• evaluation of issues concerning database applications, including security and data integrity
• modelling information using markup languages (XML and JSON)
• awareness of the differences between relational and NoSQL databases

Syllabus content

• introduction to databases
• information, data and knowledge
• database systems
• relational data model
• Structured Query Language (SQL)
• relational algebra
• database design
• conceptual database design (ER diagrams)
• logical database design (ER to SQL)
• physical database design (indexes)
• security, transactions and concurrency
• security and integrity
• transactions and recovery
• concurrency control
• markup languages and NoSQL databases
• XML, XPath and XQuery
• JSON
• NoSQL.

Delivery

Modules will be delivered through blended learning.

You will be guided through learning activities appropriate to your module, which may include:

• online resources that you work through at your own pace (eg videos, web resources, e-books, quizzes)
• online interactive sessions to work with other students and staff (eg discussions, live streaming of presentations, live-coding, team meetings)
• face-to-face small group sessions (eg help classes, feedback sessions).

Credits

20 credit module (reference CMT218)

Dates

Spring semester. Typically, Tuesday mornings, starting late January*.

Cost

£1,220 (for the 2023-24 academic year)

Assessment

A blend of assessment types which may include coursework and portfolio assessments, class tests, and/or formal examinations.

Outline description

The aim of this module is to give you an understanding of the processes and tools required to create interactive visualisations and explanations of data. The module will allow you to critically appreciate correct visualisations, and to identify biased or manipulated interpretations. It will cover the practical skills required to create visualisations using tools such as Python and JavaScript, while also examining the theory of design required.

Objectives

On successful completion of the module, you should be able to:

• describe and discuss the theory behind visualisation design
• critically analyse visualisations of data
• examine and explore data to find the best way it can be visually represented
• create static, animated and interactive visualisations of data
• critically reflect upon and discuss the merits and shortcomings of their own visualisation work.

Skills you will practise and develop

• use of appropriate tools for data analysis and visualisation
• critical analysis of visualisation
• JavaScript and Python for data access, manipulation, statistical analysis and visualisation.

Syllabus content

• encoding theory
• visualisation theory
• visualisation history
• current trends in visualisation
• use of appropriate software tools and libraries for data analysis and visualisation
• Python: Pandas, Scipy, Numpy, Matplotlib, Seaborn, Altair, Bokeh
• JavaScript: D3, Plotly, Highcharts
• retrieving and storing data (JSON, csv) using JavaScript and Python
• visualisation development.

Delivery

Modules will be delivered through blended learning. You will be guided through learning activities appropriate to your module, which may include:

• online resources that you work through at your own pace (eg videos, web resources, e-books, quizzes)
• online interactive sessions to work with other students and staff (eg discussions, live streaming of presentations, live-coding, team meetings)
• face-to-face small group sessions (eg help classes, feedback sessions).

Credits

10 credit module (reference MAT007)

Dates

Spring semester. Typically, Friday mornings, starting early March*.

Cost

£610 (for the 2023-24 academic year)

Assessment

100% written examination.

Outline description

This module will introduce you to the ways in which statistics and OR is used within Government. It will be predominately taught by staff from the Office for National Statistics (ONS) and Welsh Government (WG) and will provide a fascinating insight into the roles of Statisticians and Operational Researchers within these organisations.

Government Departments and the public sector are large employers of graduates in Statistics and Operational Research, and hence this module provides excellent training for students considering a career in this sector or for those interested in learning the kinds of methods ONS and WG utilise when producing important analyses, reports, and official releases.

Pre-requisite knowledge

The equivalent of a first-year undergraduate module on statistics and/or probability.

Objectives

On successful completion of the module, you should be able to:

• design surveys and questionnaires
• organise and examine large datasets, including matching data from diverse sources, error correction, and imputation of missing values
• define and explain key statistical indicators produced by the UK Government.

Skills you will practise and develop

• data analytics: collection, management and cleaning of data
• mathematical reasoning: construction of effective statistical indicators.

Syllabus content

Session 1 – Introductory session

This first session will give participants an overview of government statistics. It covers their purpose and key uses, some major key statistical series and the blend of administrative and survey data used. This session will cover the structure and governance of the GSS, including legislation and Code of Practice.

Session 2 – Questionnaire design

This is an introductory session on questionnaire design. The aim is to help participants develop an understanding of how surveys are designed and why design is important to the survey process. The session will cover general design principles for questions, response categories, instructions, guidance and overall questionnaire design. The importance and methods of testing questions and questionnaires will also be covered.

Session 3 – Editing and imputation

UK official statistics are generally derived from survey data, often based on large samples of people or businesses. In practice, it is impossible to collect survey data which are complete and without error. The editing process aims to identify and correct errors in the data. Modern editing techniques aim to optimise this process by editing as efficiently as possible whilst maintaining the accuracy of resulting statistics. Imputation deals with the problem of incomplete and missing responses by estimating their expected values. If applied properly, imputation reduces the threat of non-response bias. This course covers the main editing and imputation methods used in official statistics with examples relating to the population Census and key economic and social statistics.

Session 4 – Index numbers

Index numbers are a very commonly used way of presenting statistics. Very high profile examples are GDP and the Consumer Price Index. Underpinning such important indices are some intriguing concepts and challenges and this session will cover how these are handled in theory and in practice.

Session 5 – Data matching

The advent of high-powered computing has brought about major advances in the processing and analysis of information, and many organisations now maintain large numbers of datasets in vast databases or data warehouses. In sectors such as finance and healthcare, masses of data are generated as by-products of day-to-day activities and processes, however much of this information can be difficult to harness in a meaningful way.

Data matching is a technique that facilitates the linkage of information from different data sources, making it possible to create rich new virtual datasets composed of data fields taken from a number of existing datasets; datasets which would have previously been analysed separately and in isolation. The mantra of data matching is that “the whole is better than the sum of the parts”, and by bringing together previously disparate datasets, we are able to add value, for example in the ability to study as-yet unknown relationships between sets of variables.

Delivery

You will be guided through learning activities appropriate to your module, which may include:

• weekly face to face classes (e.g. labs, lectures, exercise classes)
• electronic resources to support the learning (e.g. videos, exercise sheets, lecture notes, quizzes)

You are also expected to undertake at least 50 hours of self-guided study throughout the duration of the module, including preparation of formative assessments.

Credits

20 credit module (reference MAT022)

Dates

Autumn semester. Typically, Tuesday mornings plus a lab session on either Tuesday afternoons or Friday afternoons, starting early October*.

Cost

£1,220 (for the 2023-24 academic year)

Assessment

100% written examination.

Outline description

This module will introduce a range of fundamental statistical ideas. The broad aim of the module is to provide you with:

• an understanding of the mathematical ideas that underpin some fundamental statistical methods
• proficiency in performing practical data analysis using statistical software
• the ability to communicate the results of data analysis by clearly explaining the steps they follow in their analysis.

Objectives

On successful completion of the module, you should be able to:

• formulate problems involving uncertainty within the framework of probability theory
• understand the conditions under which various statistical methods can be applied
• summarise a data set using descriptive statistics
• calculate confidence intervals and perform hypothesis tests
• identify the sources of variation in data
• fit linear models to data and evaluate the accuracy of these models
• perform variable selection and dimension reduction
• write technical reports to communicate the results of data analysis procedures.

Skills you will practise and develop

• mathematical reasoning
• practical data analysis
• the use of statistical computer packages
• written communication skills.

Syllabus content

• elementary probability
• descriptive statistics
• estimation
• hypothesis testing
• categorical data
• correlation
• analysis of variance
• regression
• principal components analysis
• non-parametric methods.

Delivery

You will be guided through learning activities appropriate to your module, which may include:

• weekly face to face classes (e.g. labs, lectures, exercise classes)
• electronic resources to support the learning (e.g. videos, exercise sheets, lecture notes, quizzes).

You are also expected to undertake at least 50 hours of self-guided study throughout the duration of the module, including preparation of formative assessments.

Credits

10 credit module (reference MAT005)

Dates

Spring semester. Typically, Thursday mornings, starting late January*.

Cost

£610 (for the 2023-24 academic year)

Assessment

100% coursework.

Outline description

Forecasting methods are utilised in a range of industries and are important tools for both Statisticians and Operational Researchers. This module will introduce you to time series models and associated forecasting methods. It will demonstrate how such models and methods can be implemented to analyse time series data, and to appreciate the different fields of applications.  Computer workshops will allow you to build and experiment with practical forecasting tools using data from a variety of applications.

Objectives

On successful completion of the module, you should be able to:

• fit models for data from a large variety of sources
• appreciate and use modern methods of statistical inference
• forecast using a range of methods, including exponential smoothing methods and ARMA and ARIMA models.

Syllabus content

• time series models: decomposition, analysis and removal of trends and seasonality
• exponential smoothing methods: single exponential, Holt and Holt-Winters methods
• autoregressive, moving average and ARMA models
• non-stationary series - ARIMA-models
• forecasting using ARIMA models.

Delivery

You will be guided through learning activities appropriate to your module, which may include:

• weekly face to face classes (e.g. labs, lectures, exercise classes)
• electronic resources to support the learning (e.g. videos, exercise sheets, lecture notes, quizzes)

You are also expected to undertake at least 50 hours of self-guided study throughout the duration of the module, including preparation of formative assessments.

Credits

20 credit module (reference CMT307)

Dates

Autumn and spring semesters. This module takes place over both semesters. Typically, Wednesday mornings and Thursday afternoons, starting early October*.

Cost

£1,220 (for the 2023-24 academic year)

Assessment

A blend of assessment types which may include coursework and portfolio assessments, class tests, and/or formal examinations.

Outline description

The field of machine learning is concerned with the study of methods for developing computer programs that are able to learn from examples or from prior experience. Machine learning lies at the basis of many of the recent successes in artificial intelligence, with applications ranging from self-driving cars to digital assistants and search engines.

This module will serve as a general introduction to machine learning, covering both traditional methods such as decision trees and support vector machines and more recent neural network based techniques.

The main focus will be on application oriented aspects of machine leaning, such as how to implement key machine learning techniques, how to choose which technique to use in a given situation, how to pre-process data, and how to evaluate the performance of a machine learning system.

In addition to these technical topics, the module will also cover some important ethical considerations, including how the choice of training data can introduce unwanted biases in real-world applications.

Objectives

On successful completion of the module, you should be able to:

• implement and evaluate machine learning methods to solve a given task
• explain the basic principles underlying common machine learning methods
• choose an appropriate machine learning method and data pre-processing strategy to address the needs of a given application setting
• reflect on the importance of data representation for the success of machine learning methods
• critically appraise the ethical implications and societal risks associated with the deployment of machine learning methods
• explain the nature, strengths and limitations of an implemented machine learning technique.

Skills you will practise and develop

• implementing machine learning tools, taking advantage of existing libraries where appropriate
• assessing the potential and limitations of machine learning technology
• disseminating a machine learning project in an effective form (e.g., report or presentation)
• critically thinking about which tools are appropriate in what contexts, and what are the possible ethical, social or economical implications.

Delivery

Modules will be delivered through blended learning. You will be guided through learning activities appropriate to your module, which may include:

• online resources that you work through at your own pace (e.g. videos, web resources, e-books, quizzes)
• online interactive sessions to work with other students and staff (e.g. discussions, live streaming of presentations, live-coding, team meetings)
• face-to-face small group sessions (e.g. help classes, feedback sessions).

Credits

20 credit module (reference CMT202)

Dates

Spring semester. Typically, Monday mornings, starting late January*.

Cost

£1,220 (for the 2023-24 academic year)

Assessment

A blend of assessment types which may include coursework and portfolio assessments, class tests, and/or formal examinations.

Outline description

The aim of this module is to familiarise you with issues and successful approaches in the design and implementation of distributed systems. Detailed case studies of widely used systems will be discussed in this module.

The course covers: the organisation of distributed systems, focusing on various architectural styles used to develop such systems; core technologies to implement distributed systems; various models and infrastructures to support Cloud computing – such as virtualisation; and emerging themes in distributed systems, such as fault tolerance and policy driven autonomic self-management.

Objectives

On successful completion of the module, you should be able to:

• demonstrate and apply knowledge about the state-of-the-art in distributed-systems architectures
• critically evaluate the issues in distributing an application across a network
• appreciate the difference between and apply various distributed computing middleware.
• demonstrate and apply knowledge of common security practices within distributed systems
• use Cloud computing environments.

Skills you will practise and develop

• design distributed systems architecture using middleware where appropriate
• compare different designs and assess them using appropriate metrics
• design and use services in a distributed environment
• specify the deployment of Cloud computing applications
• design wrappers and services which overcome heterogeneity and support interoperability of autonomous information resources.

Delivery

Modules will be delivered through blended learning. You will be guided through learning activities appropriate to your module, which may include:

• online resources that you work through at your own pace (e.g. videos, web resources, e-books, quizzes)
• online interactive sessions to work with other students and staff (e.g. discussions, live streaming of presentations, live-coding, team meetings)
• face-to-face small group sessions (e.g. help classes, feedback sessions).

Credits

10 credit module (reference MAT514)

Dates

Spring semester. Typically, Thursday mornings, starting early March*.

Cost

£610 (for the 2023-24 academic year)

Assessment

The assessment for the module comprises a class test and group coursework.

Outline description

This module teaches the use of the popular statistical packages R and Shiny to efficiently manipulate, analyse and present complex data.

Objectives

On completion of the module, you should be able to use R and Shiny to:

• manipulate large data sets efficiently
• carry out various statistical analysis of large data sets
• present data effectively and attractively.

Syllabus content

• data structures, importing and exporting data
• data wrangling: merging and partitioning data
• descriptive statistics and data visualisation
• tables
• lm and glm (R model description for linear models, logistic regression and count models)
• programming in R: objects, flow control, functions, scope, vectorisation, strings
• ggplot2 and the tidyverse
• Shiny.

Skills you will practise and develop

The course will teach techniques that will allow the efficient manipulation, analysis and presentation of data. This module will introduce you to the open source packages R and Shiny. You will develop your ability to work in a team through the group coursework.

Delivery

You will be guided through learning activities appropriate to your module, which may include:

• weekly face to face classes (eg labs, lectures, exercise classes)
• electronic resources to support hte learning (eg videos, exervise sheets, lecture notes, quizzes).

You are also expected to undertake at least 50 hours of self-guided study throughout the duration of the module, including preparation of formative assessments.

Credits

10 credit module (reference MAT009)

Dates

Spring semester. Typically, Friday mornings, starting late January*.

Cost

£610 (for the 2023-24 academic year)

Assessment

Written examination (100%).

Outline description

This module will introduce you to the concepts of modelling for healthcare and will explore models for both:

• planning and management of healthcare resources
• epidemiology and effective treatment of disease

Examples of models might include those for the prevention, early detection and treatment of disease, such as cancer, HIV/AIDS and diabetes. Resource models would include those for the planning and management of hospital beds, operating theatres, ambulances, and provision of resources in outpatient clinics and critical care units.

A historical outline will be given on the use, practicalities and limitations of both deterministic and stochastic models.  Differential equation models, Markov, semi-Markov and simulation techniques will be discussed and case studies on various topics presented. Students will also be introduced to the importance of health economics in conjunction with OR models, for example in providing cost-utility and cost-effectiveness models for health policy evaluation.

Computer lab sessions will enable you to develop and run healthcare Markov and simulation models of your own.

The module aims to encourages you to consider what makes a mathematically robust, necessarily detailed and practical model for use by healthcare professionals, and how such tools can help influence health policy.

Objectives

On completion of the module, you should be able to:

• derive analytical models of healthcare processes
• assess the role of health economics and its application in healthcare models
• construct and adapt computational models for healthcare systems and disease progression
• evaluate and critique healthcare modelling case studies and literature
• develop an awareness of the different applications in this field
• evaluate the characteristics of mathematically robust, necessarily detailed and practical models for use by healthcare professionals.

Skills you will practise and develop

• OR and analytics: derivation of analytical models and simulation of stochastic systems
• mathematical reasoning: understanding the theory and assumptions that underpin mathematical models
• use of simulation, optimisation and statistical computer packages.

Delivery

You will be guided through learning activities appropriate to your module, which may include:

• weekly face to face classes (e.g. labs, lectures, exercise classes)
• electronic resources to support the learning (e.g. videos, exercise sheets, lecture notes, quizzes).

You are also expected to undertake at least 50 hours of self-guided study throughout the duration of the module.

Credits

20 credit module (reference MAT021)

Dates

Autumn semester. Typically, Friday mornings and Monday mornings, starting early October*.

Cost

£1,220 (for the 2023-24 academic year)

Assessment

Simulation assignment (group work) 30%.

70% written exam.

Outline description

This course will introduce you to a range of fundamental Operational Research (OR) techniques, both stochastic and deterministic in nature. You will additionally gain experience in using commercial software packages to support learning and connect theoretical understanding with solving practical problems.

The module will introduce the concepts and applications of simulation. This component will include Monte Carlo, Discrete Event, System Dynamics, and Agent Based Simulation. Computer workshops will introduce you to a range of simulation software covering the different approaches taught in the lectures.

The module then focuses on linear and integer programming, dynamic programming, scheduling, and heuristics. Following an explanation and illustrations of the standard simplex method, some of its variants will be introduced and the concepts of duality explained.  Branch and bound approaches for solving integer programming problems will be developed.  For tackling sequential problems, dynamic programming will be introduced.  Scheduling problems will be discussed, and the students will be introduced to a number of algorithms for developing efficient schedules.  For complex problems, heuristic methods may be utilised, and design principles of heuristics and local search methods will be explained.

Objectives

On completion of the module, you should be able to:

• know when it is appropriate to apply a range of fundamental OR techniques, based on an understanding of their theoretical underpinnings
• construct both deterministic and stochastic models of real-life situations using simulation, mathematical programming and other optimisation techniques
• use optimisation algorithms to solve practical problems
• implement OR models and algorithms using different commercial computer packages
• present findings and recommendations in a concise manner.

Skills that will be practised and developed

• O.R. and analytics: simulation of stochastic systems; formulation and solution of optimisation problems
• mathematical reasoning: understanding the theory and assumptions that underpin optimisation algorithms
• use of simulation and optimisation computer packages
• written communication skills.

Delivery

You will be guided through learning activities appropriate to your module, which may include:

• weekly face to face classes (e.g. labs, lectures, exercise classes)
• online interactive sessions to work with other students and staff (e.g. discussions, live streaming of presentations, live-coding, team meetings).

You are also expected to undertake at least 50 hours of self-guided study throughout the duration of the module, including preparation of formative assessments.

Credits

20 credit module (reference CMT116)

Dates

Autumn semester. Typically, Tuesday mornings, starting early October*.

Cost

£1,220 (for the 2023-24 academic year)

Assessment

A blend of portfolio assessments that will include group work, individual work, and continuous assessment.

Outline description

This module aims to provide you with a systematic understanding of cyber security management, and of risk assessment and management, and with the skills to critically analyse and evaluate existing practices.

The module covers key cyber security concepts, principles, technologies and practices. The module delivers hands-on experience of conducting risk assessment for an information system, threat modelling, developing security policies of different types and strategy for an organisation.

This module enables you to practice the skills of communicating security concepts and needs to a wide range of audiences; applying common security frameworks and best practices, as well as evaluating their effectiveness; researching and analysing recent cyber security incidents, threats and vulnerabilities.

The module informs you about legal and regulatory environment surrounding the development and use of Information and Communication Technology (ICT) and information systems, as well as about ethics and responsibilities of cyber security professionals.

Objectives

On completion of the module, you should be able to:

• determine, establish and maintain appropriate information security regulations for an organisation
• identify, analyse, evaluate and manage risks related to different components of an information system (i.e. data, people, processes, hardware, software and network) accounting for current threat landscape
• identify and effectively articulate different types of threat to, and vulnerabilities of, information systems to a range of audiences (e.g. top management, end users, non-technical and technical experts)
• critically analyse a wide range of security countermeasures, select and justify appropriate security countermeasures to mitigate risks by calculating return on security investment and economic impact of a security-related incident on business
• effectively evaluate and apply popular risk assessment methodologies and information security management frameworks to case studies
• define and implement effective security policies and processes within an organisation, make and sustain argument; make judgement and propose solutions.

Skills that will be practised and developed

• application of common security frameworks to case studies
• estimating the impact of security incidents on business
• analysis of organisation's security strategy and policy
• security policy development
• calculating return on security investments
• communicating security risks
• establishing the context for risk assessment
• risk identification, estimation, evaluation
• choice of appropriate security control(s)
• risk monitoring and review
• critical analysis of an evidence-base available to a security professional
• evaluating the effectiveness of security countermeasures
• research a range of cyber security threats and vulnerabilities
• critically assess the challenges of information security and risk management
• present arguments that evidence understanding of the subject
• professionalism in the workplace
• transferable skills (listening, communication, time management, research, literature review and analysis, group work, reflective thinking and learning, report writing, critical thinking, rhetoric and argumentation).

Delivery

Modules will be delivered through blended learning. You will be guided through learning activities appropriate to your module, which may include:

• online resources that you work through at your own pace (e.g. videos, web resources, e-books, quizzes)
• online interactive sessions to work with other students and staff (e.g. discussions, live streaming of presentations, live-coding, team meetings)
• face-to-face small group sessions (e.g. help classes, feedback sessions).

Credits

20 credit module (reference CMT206)

Dates

Spring Semester. Typically, Tuesday lunchtimes, starting late January*.

Cost

£1,220 (for the 2023-24 academic year)

Assessment

A blend of assessment types which may include coursework and portfolio assessments, class tests, and/or formal examinations.

Outline description

As the digital world grows and grows, multimodal digital media is increasingly consumed across multiple platforms and devices, for example mobile phones, tablets, and mixed reality head mounted displays.

Modern interaction between users and information systems is multimodal in nature, incorporating gestures, voice, and other forms of user interface. This module aims to develop your technical, social, business and management understanding to define and deliver effective information systems from a human centric perspective.

This module takes a systems approach to Human Centric Computing and deals with relevant aspects of Human Computer Interaction (HCI), Human Perception and Visualization.

Objectives

On completion of the module, you should be able to:

• understand the complex nature of users and apply heuristics to create and evaluate inclusive and multimodal user experiences
• evaluate competing proposals for interface design and implementation
• apply human centric design methodologies in the context of current and emerging interaction technologies such as virtual and augmented reality
• demonstrate competency in the method of scientific analysis, the control of variables, analysis, and the presentation of outcomes
• understand experimental design for the subjective assessment of user experience
• select and apply suitable methodologies for the conduct and analysis of a subjective experiment.

Skills that will be practised and developed

• usability engineering and user-centred design
• cognitive and task modelling
• prototypying interfaces
• interaction design patterns for current and emerging technologies
• usability guidelines and principles
• usability evaluation techniques and universal design
• scientific and information visualisation tools and techniques
• experimental design and data analysis
• advanced interaction and display technologies
• design for accessibility
• multimodal interface design
• auditory icons and earcons
• immersion and Sense of Presence in Mixed Reality experiences
• UX for Mixed Reality experiences including selection and locomotion techniques.

Delivery

Modules will be delivered through blended learning. You will be guided through learning activities appropriate to your module, which may include:

• online resources that you work through at your own pace (e.g. videos, web resources, e-books, quizzes)
• online interactive sessions to work with other students and staff (e.g. discussions, live streaming of presentations, live-coding, team meetings)
• face-to-face small group sessions (e.g. help classes, feedback sessions).

Credits

20 credit module (reference CMT308)

Dates

Autumn and Spring semesters. Typically, Wednesday mornings and Thursdays (mornings and afternoons) during the autumn semester, starting early October – and Tuesday afternoons during the spring semester*.

Cost

£1,220 (for the 2023-24 academic year)

Assessment

A blend of assessment types which may include coursework and portfolio assessments, class tests, and/or formal examinations.

Outline description

The advent of the internet has created many new business opportunities, especially with support for externally hosted services and infrastructure (e.g. cloud and mobile services).

This module aims to equip you with an understanding of risks and vulnerabilities associated with an effective operation of a business using both in-house and externally hosted infrastructure. A broader initial perspective will be considered, ranging from consideration of risk analysis methodologies, potential impact of these risks on both business operation and data privacy/leakage.

A scenario-driven approach will be used to introduce students to cybersecurity threats and vulnerabilities, and the potential socio- economic impact of these on a business. Potential mitigation strategies will also be introduced as part of this scenario-driven approach, focusing on continuity planning and disaster recovery.

You will plan security policy and business continuity plans, which will be tested in Red v Blue scenarios both theoretically and practically.

Objectives

On completion of the module, you should be able to:

• critically analyse the potential impact of cyber threats on business continuity
• demonstrate understanding of entrepreneurial activities, and critically analyse and appraise business opportunities, including risks of outsourcing
• evaluate threats to SMEs vs. large enterprise
• develop and test security policy and business continuity plans
• reflect upon research methods and their role in innovation.

Syllabus content

• introduction to Business Continuity and an emphasis on significance of this within a business
• key security and business continuity concepts and terminology
• business continuity management lifecycle and related standards (e.g. ISO22301, ISO27001, BS25999, BS27031)
• legal cyber security landscape: ensuring compliance with regulations (GDPR, Computer Misuse act, NIS Regulation)
• business Impact Analysis (BIA), as well as tools and methods supporting its different stages
• engineering reliable and dependable systems (Resilience Engineering)
• NCSC guidance on business continuity and relevant guidance on cyber security
• specifics of cyber security and business continuity in the context of SMES and CNI
• mitigation strategies and techniques for business continuity
• digital transformation: stages, risks, opportunities and case-studies
• research methods in cyber security and business continuity
• responsible innovation and ethics
• innovation and entrepreneurship in cyber security.

Skills that will be practised and developed

• critical evaluation of the claims from proponents of new technologies and methodologies, product vendors, researchers and consultants
• derivation of appropriate legal and ethical requirements relevant to a specific situation
• research skills in quantitative and qualitative methods
• survey of academic, technical, and practitioners’ literature innovation
• entrepreneurship and commercialisation
• critical thinking
• rhetoric and argumentation
• time management
• presentation skills
• report writing, including writing academic and technical reports
• reflective practice: the ability to reflect on performance, as a means of instilling the habit of lifelong learning.

Delivery

Modules will be delivered through blended learning. You will be guided through learning activities appropriate to your module, which may include:

• online resources that you work through at your own pace (e.g. videos, web resources, e-books, quizzes)
• online interactive sessions to work with other students and staff (e.g. discussions, live streaming of presentations, live-coding, team meetings)
• face-to-face small group sessions (e.g. help classes, feedback sessions).

Credits

10 credit module (reference MAT006)

Dates

Spring semester. Typically, Tuesday afternoons during the spring semester, starting early March*.

Cost

£610 (for the 2023-24 academic year)

Assessment

2 hour written examination (100%).

Outline description

This module aims to provide students with an in-depth understanding of timely and effective supply chain modelling and optimization techniques. The course is taught using mathematical models and case studies to demonstrate the effectiveness of the models.

At the end of the course, you will be familiar with standard problems and models in supply chain modelling and management. More specifically, you will be capable to model supply chain optimisation problems mathematically and to solve these problems with appropriate algorithms and software packages. This enables you to analyse supply chains and to make sound decisions in this field.

Objectives

On completion of the module, you should be able to:

• identify the strategic, tactical and operational dimensions of supply chain modelling
• select optimal sites for service facilities
• plan the composition and scheduling of a workforce
• quantitatively analyse service quality in the context of continuous improvement
• apply revenue management approaches and solve capacity control problems in supply chains
• quantify the value of stochastic programming in supply chain modelling
• present findings and recommendations in a concise manner to non-experts in the area.

Syllabus content

• introduction to supply chain modelling
• site selection of facilities
• workforce planning and scheduling
• service quality and continuous improvement
• revenue management.

Skills that will be practised and developed

• OR and analytics: modelling and optimisation of supply chains
• mathematical reasoning: understanding the theory and assumptions that underpin supply chain models and algorithms
• application of optimisation computer packages to supply chains
• written communication skills.

Delivery

You will be guided through learning activities appropriate to your module, which may include:

• weekly face to face classes (e.g. labs, lectures, exercise classes)
• electronic resources to support the learning (e.g. videos, exercise sheets, lecture notes, quizzes).

You are also expected to undertake at least 50 hours of self-guided study throughout the duration of the module, including preparation of formative assessments.

Credits

10 credit module (reference MAT012)

Dates

Spring semester. Typically, Thursday afternoons and Monday afternoons, starting late January*.

Cost

£610 (for the 2023-24 academic year)

Assessment

100% coursework.

Outline description

The course aim is to present a comprehensive review of the objectives, methods and practical implementations of consumer credit and behavioural scoring in particular and data mining in general. It involves understanding how large data sets can be used to model customer behaviour and how such data is gathered, stored and interrogated and its use to cluster, segment and score individuals.

Credit scoring is the process of deciding, whether or not to grant or extend a credit product by a financial lender. Sophisticated mathematical and statistical models have been developed to assist in such decision-making activities.

Objectives

On completion of the module, you should be able to:

• work with statistical software to develop credit scoring solutions
• develop a scorecard using advanced data mining techniques
• critique how financial firms such as mortgage lenders make business decisions based on credit scoring techniques
• summarise improvements in regulating credit risk by financial regulatory bodies
• detail practical difficulties that arise when implementing scorecards
• identify cross-fertilisation potential to other business contexts (e.g. fraud detection, CRM, marketing).

Syllabus content

• introduction data mining and credit socring
• statistical methods for scorecard development
• practical issues of scorecard performance
• measuring scorecard performance
• behavioural scoring and profit scoring
• survival analysis approaches
• Basel Accord and other applications of scoring methodology.

Skills that will be practised and developed

Subject specific intellectual (cognitive) skills:

• prepare data prior to model building
• apply supervised learning algorithms to constructing scoring rules
• assess scorecard performance
• appraise new applications of credit scoring techniques.

Transferable (key/general) skills:

• express complex technical details clearly in written form
• detail research to support written arguments
• demonstrate effective time-management.

Delivery

You will be guided through learning activities appropriate to your module, which may include:

• weekly face to face classes (e.g. labs, lectures, exercise classes)
• electronic resources to support the learning (e.g. videos, exercise sheets, lecture notes, quizzes).

You are also expected to undertake at least 50 hours of self-guided study throughout the duration of the module, including preparation of formative assessments.

Credits

10 credit module (reference MAT031)

Dates

Autumn semester. Typically, Thursday afternoons, starting early October*.

Cost

£610 (for the 2023-24 academic year)

Assessment

2 hour written examination (100%).

Outline description

This course dovetails with the module MAT021 Foundations of Operational Research and Analytics and will introduce you to a number of further Operational Research (OR) techniques, specifically: queueing theory, project management, game theory and inventory control.

In each case you will be introduced to the underlying method, the theoretical underpinnings and practical applications. In the queueing theory section, the components of a queueing system will be defined and single server queues will be considered using Markov chain methods. These will be extended to multiple servers and including waiting spaces.

The project management component will consider the activities that make up a project and how to derive network representations. Floats and critical paths will be determined and the concept of crashing will be discussed.

In game theory, you will be introduced to the normal form of a game, Nash equilibria and dominance. Mixed strategies, the equality of payoffs theorem and the prisoner’s dilemma will also be discussed.

Inventory theory is used to determine policies to minimise the cost of running an inventory system while meeting customer demand and for some similar optimisation problems. The final part of the module will cover several inventory theory models including the economic order quantity model, the continuous production rate model and the newsboy model.

Objectives

On completion of the module, you should be able to:

• work with statistical software to develop credit scoring solutions
• develop a scorecard using advanced data mining techniques
• critique how financial firms such as mortgage lenders make business decisions based on credit scoring techniques
• summarise improvements in regulating credit risk by financial regulatory bodies
• detail practical difficulties that arise when implementing scorecards
• identify cross-fertilisation potential to other business contexts (e.g. fraud detection, CRM, marketing).

Syllabus content

• evaluate when it is appropriate to apply a range of OR techniques, based on an understanding of their theoretical underpinnings
• create models of queueing situations and derive solutions to such models
• apply decision making processes to queueing systems
• produce node on arrow diagrams of activities making up a project and identify the critical path
• calculate the most efficient way of reducing the duration of projects
• represent games in Normal Form and compute dominant strategies
• define and determine pure and mixed Nash equilibria
• determine when orders should be placed and the size of each order using various inventory models and related modelling techniques.

Skills that will be practised and developed

Problem solving, logical thinking. Applying mathematical concepts to real-life situations.

Syllabus content

• Queueing Theory. Elements of a queueing system, continuous time Markov processes, exponential distribution, single server queues, multiple server queues, applications
• Project Management. Drawing and analysing networks, earliest and latest event times, floats, crashing
• Game Theory. Nash equilibria, dominance, mixed strategies, equality of payoffs
• Inventory Control. EOQ models, newsboy models, using LP/IP modelling tools, inventory models with stochastic demand.

Delivery

You will be guided through learning activities appropriate to your module, which may include:

• weekly face to face classes (e.g. labs, lectures, exercise classes)
• electronic resources to support the learning (e.g. videos, exercise sheets, lecture notes, quizzes).

You are also expected to undertake at least 50 hours of self-guided study throughout the duration of the module, including preparation of formative assessments.