Advanced Computer Science with Internet Economics MSc

  • Programme duration: Full-time: 12 months   Part-time: 24 months
  • Programme start: September 2020
  • Entry requirements: You will need a 2:1 honours degree (or above) in a subject area closely related to Computer Science, Economics, or the intersection of these two subject areas.
Advanced Computer Science with Internet Economics

Module details

Compulsory modules

Introduction to Computational Game Theory (COMP323)
Level3
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:20
Aims

To introduce the student to the notion of a game, its solutions, concepts, and other basic notions and tools of game theory, and the main applications for which they are appropriate, including electricity trading markets.

To formalize the notion of strategic thinking and rational choice by using the tools of game theory, and to provide insights into using game theory in modeling applications.

To draw the connections between game theory, computer science, and economics, especially emphasizing the computational issues.

To introduce contemporary topics in the intersection of game theory, computer science, and economics.

Learning Outcomes

(LO1) A student will understand the notion of a strategic game and equilibria, and understand the characteristics of main applications of these concepts;

(LO2) Given a real world situation a student should be able to identify its key strategic aspects and based on these be able to connect them to appropriate game theoretic concepts;

(LO3) A student will understand the key connections and interactions between game theory, computer science and economics;

(LO4) A student will understand the impact of game theory on its contemporary applications, and be able to identify the key such application areas;

(S1) Numeracy/computational skills - Problem solving

(S2) Critical thinking and problem solving - Creative thinking

(S3) Numeracy/computational skills - Reason with numbers/mathematical concepts

Microeconomic Theory (ECON915)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting80:20
Aims

This module aims to provide an opportunity to understand and appreciate the fundamental aspects of decision making in an uncertain environment, allowing for the possible synchronic or diachronic incidence of risk. Individual risk linked behaviour will be linked to symmetric and asymmetric imperfect information scenarios. In this context individual or circumscribed group behaviour may be related to an aggregate and institutional context.

Learning Outcomes

(LO1) An appreciation of the basic aspects of consumer and producer decision making both under certainty and uncertainty;

(LO2) An understanding of the underlying assumptions needed to justify the existence of a general competitive equilibrium;

(LO3) An understanding of the relationship between a general equilibrium and welfare considerations.

(S1) Problem solving skills

(S2) Numeracy

(S3) Organisational skills

(S4) Communication skills

(S5) Lifelong learning skills

Object Oriented Software Development (COMP517)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:100
Aims

To provide a deep and systematic understanding of the software development process from initial specification and design to final system testing using sound software engineering methods and techniques following the Object Oriented paradigm.

Learning Outcomes

(LO1) The module addresses learning outcome 1 for the MSc in Computer Science programme with respect to the entry route for students without a CS first degree. At the end of the module students should be able to design, implement and test reasonably complicated software system;

(LO2) have a critical understanding of the Object Oriented Programming (OOP) paradigm;

(LO3) be able to analyse critically reasonably complex software systems;

(LO4) be able to demonstrate sound programming skills.

(S1) Problem Solving - Numeracy and computational skills

(S2) Problem solving – analysing facts and situations and applying creative thinking to develop appropriate solutions.

Optimisation (COMP557)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:25
Aims

To provide a foundation for modelling various continuous and discrete optimisation problems.

To provide the tools and paradigms for the design and analysis of algorithms for continuous and discrete optimisation problems. Apply these tools to real-world problems.

To review the links and interconnections between optimisation and computational complexity theory.

To provide an in-depth, systematic and critical understanding of selected significant topics at the intersection of optimisation, algorithms and (to a lesser extent) complexity theory, together with the related research issues.

Learning Outcomes

(LO1) The ability to recognise potential research opportunities and research directions

(LO2) The ability to read, understand and communicate research literature in the field of optimisation.

(LO3) The ability to use appropriate algorithmic paradigms and techniques in context of a particular optimisation model.

(LO4) The ability to formulate optimisation models for the purpose of modelling particular applications.

(LO5) A critical awareness of current problems and research issues in the field of optimisation.

(S1) Critical thinking and problem solving - Critical analysis

(S2) Communication (oral, written and visual) - Presentation skills – oral

Research Methods in Computer Science (COMP516)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:100
Aims

1. To provide a deep and systematic undersanding of the nature and conduct of CS research. 2. To enhance existing transferable key skills. 3. To develop high order transferable key skills. 4. To equip students with the ability to undertake independent research. 5. To remind students of the Legal, Social, Ethical and Profesional (LSEP) issues applicable to the computer industry.

Learning Outcomes

(LO1) Have an understanding of how established techniques of research and enquiry are used to extend, create and interpret knowledge in Computer Science.

(LO2) Have a conceptual understanding sufficient to:(i) evaluate critically current research and advanced scholarship in Computer Science and (ii) propose possible alternative directions for further work.

(LO3) Be able to: (i) deal with complex issues at the forefront of the academic discipline of Computer Science in a manner, based on sound judgements, that is both systematic and creative, (ii) demonstrate self-direction and originality in tackling and solving problems within the domain of Computer Science, (iii) act autonomously in planning and implementing solutions in a professional manner and (iv) define, plan, and/or carry out a project related to research and to communicate conclusions clearly to both specialists and non-specialists.

(LO4) Make use of the qualities and transferable skills necessary for employment requiring:(i) the exercise of initiative and personal responsibility, (ii) decision making in complex and unpredictable situations, (iii) scientific risk identification, assessment and control, and (iv) the independent learning ability required for continuing professional development.

(LO5) Understand and participate within the professional, legal, social and ethical framework within which they would be expected to operate as professionals within the IT industry.

(LO6) Have the skills set to be able to continue to advance their knowledge and understanding, and to develop new skills to a high level, with respect to continuing professional development as a "self-directed life-long learner" across the discipline of Computer Science.

(S1) Communication (oral, written and visual) - Presentation skills – oral

(S2) Communication (oral, written and visual) - Listening skills

(S3) Communication (oral, written and visual) - Academic writing (inc. referencing skills)

(S4) Time and project management - Project planning

(S5) Research skills - Ethical awareness

MSc Project (COMP702)
LevelM
Credit level60
SemesterSummer (June-September)
Exam:Coursework weighting0:100
Aims

To give students the opportunity to work in a guided but independent fashion to explore a substantial problem in depth, making practical use of principles, techniques and methodologies acquired elsewhere in the programme.
To give experience of carrying out a large piece of individual work and in producing a dissertation.
To enhance communication skills, both oral and written.

Learning Outcomes

(LO1) After completing the module students should be able to: Investigate and specify a substantial problem in the domain of Computer Science, to place it in the context of related work including, as appropriate, Computer Science reserach, and to produce a plan to address this problem

(LO2) Make use of the qualities and transferable skills necessary for the conduct of a Computer Science project: (i) the exercise of initiative and personal responsibility, (ii) decision making in complex situations, (iii) risk identification (including, as appropriate, commercial and scientific risk), assessment and control, and (iv) the independent learning ability required for continuing professional development

(LO3) Demonstrate effective time management, self-direction and originality in carrying out a project in the domain of Computer Science

(LO4) Locate and make use of information relevant to a given IT project

(LO5) Design a solution to a substantial IT problem

(LO6) Implement and test potential solutions to IT problems

(LO7) Evaluate critically, as relevant to the project, current research and advanced scholarship in Computer Science, evaluate their own work, and participate effectively in the process of peer review of other projects

(LO8) Conduct and evaulate critically the project within the professional, legal, social and ethical framework in Computer Science and Sortware Engineering

(LO9) Prepare and deliver formal presentations

(LO10) Prepare and deliver a demonstration of software

(LO11) Structure and write a dissertation describing their project

(S1) Communication (oral, written and visual) - Presentation skills – oral

(S2) Communication (oral, written and visual) - Presentation skills - written

(S3) Communication (oral, written and visual) - Academic writing (inc. referencing skills)

(S4) Time and project management - Project planning

(S5) Critical thinking and problem solving - Critical analysis

(S6) Critical thinking and problem solving - Evaluation

(S7) Commercial awareness - Ability to analyse/balance risk and reward

Optional modules

Advanced Algorithmic Techniques (COMP523)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:24
Aims

To provide a sound foundation concerning the design and analysis of advanaced discrete algorithms.
To provide a critical rational concerning advanced complexity theory and algorithmics.
To provide an in-depth, systematic and critical understanding of selected significant issues at the forefront of research explorations in the design and analysis of discrete algorithms.

Learning Outcomes

(LO1) Describe the following classes of algorithms and design principles associated with them: recursive algorithms, graph (search-based) algorithms, greedy algorithms, algorithms based on dynamic programming, network flow (optimisation) algorithms, approximation algorithms, randomised algorithms, distributed and parallel algorithms.

(LO2) Illustrate the above mentioned classes by examples from classical algorithmic areas, current research and applications.

(LO3) Identify which of the studied design principles are used in a given algorithm taking account of the similarities and differences between the principles.

(LO4) Apply the studied design principles to produce efficient algorithmic solutions to a given problem taking account of the strengths and weaknesses of the applicable principles.

(LO5) Outline methods of analysing correctness and asymptotic performance of the studied classes of algorithms, and apply them to analyse correctness and asymptotic performance of a given algorithm.

(S1) Critical thinking and problem solving - Critical analysis

(S2) Critical thinking and problem solving - Evaluation

(S3) Critical thinking and problem solving - Problem identification

(S4) Critical thinking and problem solving - Creative thinking

(S5) Numeracy/computational skills - Reason with numbers/mathematical concepts

(S6) Numeracy/computational skills - Problem solving

Web Programming (COMP519)
LevelM
Credit level15
SemesterFirst Semester
Exam:Coursework weighting0:100
Aims

To provide students with a deep, critical and systematic understanding of the most significant technologies for developing web applications.
To enable students to use these technologies in the development of web applications.
To provide knowledge of the characteristics of good web site design principles.

Learning Outcomes

(LO1) be able to use a range of technologies and programming languages available to organisations and businesses and be able to choose an appropriate architecture for a web application.

(LO2) be able to develop reasonably sophisticated client-side web applications using one or more suitable technologies and to make informed and critical decisions in that context.

(LO3) be able to develop reasonably sophisticated server-side web applications using one or more suitable technologies and to make informed and critical decisions in that context.

(S1) Problem Solving - Numeracy and computational skills

(S2) Problem solving – Analysing facts and situations and applying creative thinking to develop appropriate solutions.

Applied Algorithmics (COMP526)
LevelM
Credit level15
SemesterSecond Semester
Exam:Coursework weighting75:25
Aims

The main aim of this module is to lay down a strong context for research explorations in the field of algorithms. This is done through a rigorous study of selected algorithmic solutionswith application to related fields requiring analysis of large data (bioinformatics, networking, data compression, etc). This will be done by provision of the rationale for the use of algorithmic design and analysis methods, and also an in-depth, systematic and critical study of several important algorithmic challenges residing on the border of the theory of abstract algorithms and engineering of applied algorithmic solutions.

Learning Outcomes

(LO1) Critical awareness of algorithmic problems and as well as research issues in the context of engineering of efficient algorithmic solutions.

(LO2) Clear understanding of the relation (including differences) between the goals in the design of efficient abstract and applied algorithmic solutions.

(LO3) Ability to understand and assimilate research literature relating to the application of algorithmic techniques.

(LO4) Ability to undertake small software projects.

(LO5) Ability to communicate (within and outside of Algorithms/CS community) problems related to efficiency of algorithmic solutions

(S1) Critical thinking and problem solving - Critical analysis

(S2) Critical thinking and problem solving - Problem identification

(S3) Critical thinking and problem solving - Evaluation

(S4) Critical thinking and problem solving - Creative thinking

(S5) Numeracy/computational skills - Problem solving

Data Mining and VIsualisation (COMP527)
LevelM
Credit level15
SemesterSecond Semester
Exam:Coursework weighting75:25
Aims

To provide an in-depth, systematic and critical understanding of some of the current research issues at the forefront of the academic research domain of data mining.

Learning Outcomes

(LO1) A critical awareness of current problems and research issues in Data Mining.

(LO2) A comprehensive understanding of current advanced scholarship and research in data mining and how this may contribute to the effective design and implementation of data mining applications.

(LO3) The ability to consistently apply knowledge concerning current data mining research issues in an original manner and produce work which is at the forefront of current developments in the sub-discipline of data mining.

(LO4) A conceptual understanding sufficient to evaluate critically current research and advanced scholarship in data mining.

(S1) Critical thinking and problem solving - Problem identification

(S2) Critical thinking and problem solving - Critical analysis

Multi-agent Systems (COMP310)
Level3
Credit level15
SemesterSecond Semester
Exam:Coursework weighting100:0
Aims

To introduce the student to the concept of an agent and multi-agent systems, and the main applications for which they are appropriate.

To introduce the main issues surrounding the design of intelligent agents.

To introduce the main issues surrounding the design of a multi-agent society.

To introduce a contemporary platform for implementing agents and multi-agent systems.

Learning Outcomes

(LO1) Understand the notion of an agent, how agents are distinct from other software paradigms (eg objects) and understand the characteristics of applications that lend themselves to an agent-oriented solution; Understand the key issues associated with constructing agents capable of intelligent autonomous action, and the main approaches taken to developing such agents; Understand the key issues in designing societies of agents that can effectively cooperate in order to solve problems, including an understanding of the key types of multi-agent interactions possible in such systems Understand the main application areas of agent-based solutions, and be able to develop a meaningful agent-based system using a contemporary agent development platform.

Technologies for E-commerce (COMP315)
Level3
Credit level15
SemesterSecond Semester
Exam:Coursework weighting100:0
Aims

To introduce the environment in which e-commerce takes place, the main technologies for supporting e-commerce, and how these technologies fit together.

To introduce security as a major issue in secure e-commerce, and to provide an overview of security issues.

To introduce encryption as a means of ensuring security, and to describe how secure encryption can be delivered.

To introduce issues relating to privacy.

To introduce auction protocols and negotiation mechanisms as emerging e-commerce technologies.

Learning Outcomes

(LO1) Upon completing this module, a student will: understand the main technologies behind e-commerce systems and how these technologies interact; understand the security issues which relate to e-commerce; understand how encryption can be provided and how it can be used to ensure secure commercial transactions; understand implementation aspects of e-commerce and cryptographic systems; have an appreciation of privacy issues; and understand auction protocols and interaction mechanisms.


Note: Up to 30 credits of the recommended advanced modules may be replaced with selected level 3 modules with the proviso that a graduate of the University cannot elect to take a level three module if they have already passed that module as part of their undergraduate study.