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Data Analytics for Government

We are pleased to offer 14 modules from the MSc Data Analytics for Government (MDataGov), available to study on a standalone basis for continuing professional development (CPD).

We have developed this programme in conjunction with the Office of National Statistics (ONS).

The modules on offer include four core and ten optional modules. CPD students wishing to accumulate credits towards a qualification (either a Postgraduate Certificate, Diploma, or full MSc) must study and pass the four core modules before studying optional modules.

These modules are suitable for those who want to upskill or further their career, and who are happy to study alongside full-programme MSc students at the University. The content has been designed for those working in the UK public sector, although anyone is eligible to apply.

Each module is worth either 10 or 20 credits. We are working towards a system that will enable you to accumulate credits towards a postgraduate qualification over a period of time, should you wish.

The MSc is also available as one-year full-time programme.

Core modules

Credits

10 credit module (reference MAT032)

Dates

Autumn semester

Cost

£570 (for the 2022/23 academic year)

Assessment

Written examination 80% (two hours)

Coursework 20% (two pieces of written work)

It will be assumed that you will be taking the assessment for the module.

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: the collection of data using surveys and the estimation of population variables.
  • Mathematical reasoning: calculation of probabilities for sampling events.

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

The precise mode of delivery and details – subject to Welsh Government and Public Health Wales guidance – of the teaching and support activities will be made available at the start of the semester.

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 CMT314)

Dates

Autumn and spring semesters

Cost

£570 (for the 2022/23 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

The precise mode of delivery and details – subject to Welsh Government and Public Health Wales guidance – of the teaching and support activities will be made available at the start of the semester.

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 CMT315)

Dates

Autumn and Spring semesters

Cost

£570 (for the 2022/23 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.

Syllabus content

  • Basic programming in Python: Fundamental data types, program control structures, Object Oriented Programming and other basic language features
  • Data extraction and importing; analysis using common libraries (e.g. Pandas, Numpy/Scipy)
  • Descriptive statistics
  • Hypothesis testing
  • Natural language processing using common libraries (e.g. NLTK, SpaCY)
  • Retrieving data from online sources (web scraping, APIs).

Delivery

The precise mode of delivery and details – subject to Welsh Government and Public Health Wales guidance – of the teaching and support activities will be made available at the start of the semester.

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

Cost

£570 (for the 2022/23 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

  • 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

Teaching will be delivered through interactive lectures and seminars.

This module will be delivered through a mixture of synchronous and asynchronous activities, which may include on-campus and online teaching and support.

The precise mode of delivery and details – subject to Welsh Government and Public Health Wales guidance – of the teaching and support activities will be made available at the start of the semester.

There will also be regular virtual ‘office hours’ during which module convenors will be available.

Optional modules

Credits

20 credit module (reference CMT220)

Dates

Spring semester

Cost

£1,140 (for the 2022/23 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

The precise mode of delivery and details – subject to Welsh Government and Public Health Wales guidance – of the teaching and support activities will be made available at the start of the semester.

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

Cost

£1,140 (for the 2022/23 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

The precise mode of delivery and details – subject to Welsh Government and Public Health Wales guidance – of the teaching and support activities will be made available at the start of the semester.

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

Cost

£570 (for the 2022/23 academic year)

Assessment

100% written examination.

Outline description

This module will introduce the student 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 Assembly Government (WAG), and will therefore provide a first-hand fascinating insight in to the roles of Statisticians and Operational Researchers within these organisations. Government is a large employer of graduates in Statistics/OR, and hence this module provides excellent training for students considering a career in Government or for those interested to know what kinds of methods ONS and WAG utilise in assisting them to produce important analyses and reports.

Objectives

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

  • Appreciate the ways in which Statistics and OR is used within Government
  • Understand the methods used in undertaking surveys and handling large datasets
  • Appreciate the nature of key statistical indicators produced by Government and the various reports produced.

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

20 hours of lectures, practical workshops and case studies. The precise mode of delivery and details – subject to Welsh Government and Public Health Wales guidance – of the teaching and support activities will be made available at the start of the semester.

Credits

20 credit module (reference MAT022)

Dates

Autumn semester

Cost

£1,140 (for the 2022/23 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 students 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 written report.

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.

Syllabus content

  • Elementary probability
  • Descriptive statistics
  • Estimation
  • Hypothesis testing
  • Categorical data
  • Correlation
  • Analysis of variance
  • Regression
  • Principal components analysis
  • Non-parametric methods.

Delivery

The precise mode of delivery and details – subject to Welsh Government and Public Health Wales guidance – of the teaching and support activities will be made available at the start of the semester.

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, group meetings)
  • Face-to-face small group sessions (eg tutorials, exercise classes, feedback sessions).

Credits

10 credit module (reference MAT005)

Dates

Spring semester

Cost

£570 (for the 2022/23 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 the students 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 for students to appreciate the different fields of applications.  Computer workshops will allow students 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

The precise mode of delivery and details – subject to Welsh Government and Public Health Wales guidance – of the teaching and support activities will be made available at the start of the semester.

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, group meetings)
  • Face-to-face small group sessions (eg tutorials, exercise classes, feedback sessions).

Credits

20 credit module (reference CMT307)

Dates

Autumn and Spring semesters

Cost

£1,140 (for the 2022/23 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

The precise mode of delivery and details – subject to Welsh Government and Public Health Wales guidance – of the teaching and support activities will be made available at the start of the semester.

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 CMT202)

Dates

Spring semester

Cost

£1,140 (for the 2022/23 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

The precise mode of delivery and details – subject to Welsh Government and Public Health Wales guidance – of the teaching and support activities will be made available at the start of the semester.

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 MAT514)

Dates

Spring semester

Cost

£570 (for the 2022/23 academic year)

Assessment

100% 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 successful 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.

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 students to the open source packages R and Shiny.

Delivery

The precise mode of delivery and details – subject to Welsh Government and Public Health Wales guidance – of the teaching and support activities will be made available at the start of the semester.

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 MAT009)

Dates

Spring semester

Cost

£570 (for the 2022/23 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 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 HIV/AIDS, Diabetes and Asthma.  Resource models would include those for the planning and management of hospital beds, operating theatres 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.

Through computer lab sessions, students will be able to develop and run healthcare models of their own.  The unit aims to encourage the student 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:

  • Appreciate a variety of approaches to modelling for healthcare
  • Understand the process of mathematical modelling in this context
  • Demonstrate an awareness of the principal concepts in health economics and their application to healthcare modelling.
  • Develop and run healthcare models
  • Evaluate and critique models for their use in the health sector
  • Demonstrate an awareness of the different applications in this field
  • Appreciate and identify the characteristics of mathematically robust, necessarily detailed and practical models for use by healthcare professionals.

Delivery

The precise mode of delivery and details – subject to Welsh Government and Public Health Wales guidance – of the teaching and support activities will be made available at the start of the semester.

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 MAT021)

Dates

Autumn semester

Cost

£1,140 (for the 2022/23 academic year)

Assessment

30% coursework.

70% written exam.

Outline description

This course will introduce to students a range of fundamental Operational Research (OR) techniques, both stochastic and deterministic in nature. Students 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 the students 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

The precise mode of delivery and details – subject to Welsh Government and Public Health Wales guidance – of the teaching and support activities will be made available at the start of the semester.

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).

Entry requirements

You must have a 1st or upper 2nd class UK honours degree or equivalent in a numerate subject, such as mathematics, operational research, statistics, computer science, management science, economics, engineering or a suitable science degree, or equivalent professional experience.

Or, if you are applying solely on the basis of your professional experience, you must have been working in a relevant role for at least a year. If you are unsure as to whether your qualifications or professional experience are relevant, please contact admissions@cardiff.ac.uk.

CPD students wishing to accumulate credits towards the full MSc (180 credits) must pass 120 credits of modules, including the four core modules, before progressing to the 60-credit dissertation. Students wishing to accumulate credits towards a PG Certificate (60 credits) or PG Diploma (120 credits) must also pass the four core modules, plus additional optional modules.

CPD students are not permitted to study more than 60 credits in an academic year.

Applicants whose first language is not English must meet our English Language requirements.

How to apply

Please use the online application form to apply for one or more modules.

We have provided some Guidance Notes to help clarify and simply your application experience.

For Autumn semester modules you will need to apply by Friday 23 September 2022, and for Spring semester modules you will need to apply by mid-January 2023. Applications received after these dates will be considered for the 2023-24 intake.

Please note that CPD students who wish to continue their studies need to re-apply every year. Applications open in June. For autumn semester modules you will need to apply by early September, and for spring semester modules you will need to apply by mid-January.

If your organisation is funding the study of your module(s) then you will need to confirm this by uploading a simple sponsorship letter which clearly states the total amount (£) your employer/department will pay towards your studies, so please organise this before you apply.

It will be assumed that you will be taking the assessment for each module. We are currently working on a process for those who do not want to wish to take the assessment, and we will provide further details during induction.

All the information on this page is correct at time of publication, however module details are subject to change.

Contact us

Please contact the Admissions team for further guidance on the application process:

Applicant enquiries