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Computer Science and Statistics

Note: The School reserves the right to remove or add modules as required. Visiting students cannot participate on part (one semester/term) of a full year module. All students must complete the full module in order to receive the ECTS.
Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

Introduction to Computing I CS1021

5 (Michaelmas Term) 2 lectures; 1 tutorial; 1 laboratory hour N/A Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

Course Description and Learning Outcomes

Specific topics addressed in this module include: Number systems, memory and data representation; Basic computer architecture (CPU, memory, registers, fetch-decode-execute loop); Assembly language and machine code; Binary arithmetic and bit-wise operations; Program flow control using branch instructions; Memory accesses (using load and store instructions).
Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

Electrotechnology CS1025

5 (Michaelmas Term) 2 lectures; 1 tutorial; 2 laboratory hour N/A Course work; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

Course Description and Learning Outcomes

Basic circuit theory (AC & DC); electric & magnetic field theory; elementary semi-conductor operation and optical principles.
Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

Computers and Society CS1081

5 (Michaelmas Term) 3 lectures N/A Coursework Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

Course Description and Learning Outcomes

IT and its “impact” on society; models for assessing technological “impact”; history of IT; ethics; writing, presenting and argumentation.
Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

Software Applications 1 ST1001

5 (Michaelmas Term) 2 Laboratory hours N/A Continuous assessment Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

Course Description and Learning Outcomes

IT and its “impact” on society; models for assessing technological “impact”; history of IT; ethics; writing, presenting and argumentation.
Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

Statistical Analysis 1 ST1002

5 (Michaelmas Term)2 lectures; 3 laboratory hours N/A Examination; Continuous assessment Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

Course Description and Learning Outcomes

The aim of the course is to introduce the students to basic statistical concepts. There will be considerable emphasis on the use of a statistical package to analyse data.
Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

Introduction to Statistics I ST1251

5 (Michaelmas Term)3 lectures; 1 tutorial N/A Examination; Course work Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

Course Description and Learning Outcomes

To introduce students to the elementary ideas of probability and the use of simple probability models.
Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

Systems Programming I CS2014

5 (Michaelmas Term) 2 lectures; 2 laboratory hours CS1010, CS1021, CS1022 Module Co-Requisite: CS2010 Examination; Course work Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

Course Description and Learning Outcomes

To introduce students to the elementary ideas of probability and the use of simple probability models.
Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

Telecommunications II CS2031

5 (Michaelmas Term) 2 lectures; 2 lectures; 1 tutorial; 2 laboratory hours CS1025 and CS1031 Examination; Course work Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

Course Description and Learning Outcomes

A two part course - the first half of which is a telecommunications course examining the data link, network and transport layers of the OSI network model, and the second half focuses on the methods and techniques for efficient management (storage and retrieval) of data and information in a computer and on the world wide web.
Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

Information Management I CS2041

5 (Michaelmas Term) 2 lectures; 1 tutorial Programming Language such as Java or C Examination; Course work Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

Course Description and Learning Outcomes

This part of the course focuses on the methods and techniques for efficient management (storage, manipulation and retrieval) of data and information in a computer and on the worldwide web.
Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

Computer Engineering II CS2E03

5 (Michaelmas Term) 3 Lectures; 1 tutorial; 1 lab N/A Examination; Course work Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

Course Description and Learning Outcomes

Number systems, data representation and basic computer architecture; Migrating from C to C++; C++ classes, constructors, destructors, overloading, inheritance; Dynamic and stack based memory allocation (e.g. malloc, free, new and delete); File I/O; String, list, stack, queue and tree data structures; Algorithm complexity; Simple 2D graphics; Multi-core programming.
Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

Applied Probability 1 ST2004

5 (Michaelmas Term) Total Lecture hours: 27. Total Lab hours: 6. Total hours: 33 Elementary mathematics including integration. Examination; Course work Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

Course Description and Learning Outcomes

In this course, we take a problem-based approach that replaces mathematics with the use of random numbers in a spreadsheet, by following what is known as the Monte Carlo method. Students will rapidly acquire the facility to model complex random (or stochastic) systems. They will subsequently learn the language of probability which can sometimes by-pass the algorithms, or render them more efficient.
Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

Probability and Theoretical Statistics I ST2351

5 (Michaelmas Term) 2 lectures; 1 laboratory hour ST1351, ST1352 Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

Course Description and Learning Outcomes

In this course, we take a problem-based approach that replaces mathematics with the use of random numbers in a spreadsheet, by following what is known as the Monte Carlo method. Students will rapidly acquire the facility to model complex random (or stochastic) systems. They will subsequently learn the language of probability which can sometimes by-pass the algorithms, or render them more efficient.
Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

Symbolic Programming CS3011

5 (Michaelmas Term) 2 lectures; 1 laboratory hour Some programming experience. Coursework; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

Course Description and Learning Outcomes

Basic introduction to Prolog including recursion, definite clause grammars, cuts and negation.
Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

Software Engineering CS3012

5 (Michaelmas Term) 2 lectures; 1 tutorial N/A Coursework; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

Course Description and Learning Outcomes

This course provides students with a solid grounding in various aspects related to building large, important software systems.
Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

Introduction to Functional Programming CS3016

5 (Michaelmas Term) Total hours: 33 N/A Coursework; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

Course Description and Learning Outcomes

On successful completion of this module students will be able to:
  • • Develop programs in a high level functional language;
  • • Analyse and structure program designs in terms of functional concepts;
  • • Understand the concept of higher-order programming inherent in functional languages;
  • • Improve software modularity and reusability by applying higher-order principles to refactor code;
  • • Apply a number of functional programming techniques and tools to develop effective functional systems.
  • Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Computer Architecture II CS3021

    5 (Michaelmas Term) 2 lectures; 1 tutorial Assembly language and C/C++ programming Coursework; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This course examines modern microprocessor system architectures, with an emphasis on instruction level pipelining, caches, multiprocessor systems and virtual memory.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Information Management II CS3041

    5 (Michaelmas Term) 3 lectures N/A Coursework; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    The course will enable students design information models and implement these models in object/relational databases as well as in less structured content environments (e.g. on the Web, in content repositories). The course will also enable student analyse and evaluate approaches to information organisation, storage, transaction support and management.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Compiler Design I CS3071

    5 (Michaelmas Term) 2 lectures; 1 tutorial N/A A basic understanding of machine architectures along with a thorough knowledge of programming in both assembly language and in high level programming languages such as C, C#, C++ or JAVA. Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    An introductory course based on attributed translation grammars. The main topics covered include finite state automata and lexical analysis, syntax and semantic analysis, recursive descent parsing, symbol-table management and simple object code generation techniques.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Forecasting ST3010

    5 (Michaelmas Term) 2 lectures; 1 laboratory hour Basic Statistics and Mathematics Examination; Continuous assessment Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    Applied Forecasting (AF) module runs for 12 weeks. Several methods of forecasting will be examined, including exponential smoothing and its Holt-Winters extension, auto-regression, moving average, and further regression based methods that take into account seasonal trends of lagged variables. The module will be practical, and will involve every student in extensive analysis of case study material for a variety of time series data.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    MLA Multivariate Linear Analysis ST3011

    5 (Michaelmas Term)2 lectures; 1 laboratory hour N/A Examination; Continuous assessment Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    Classical multivariate techniques of discriminant analysis, principal component analysis, clustering and logistic regression are examined. There is a strong emphasis on the use and interpretation of these techniques. More modern techniques, some of which address the same issues, are covered in the SS module Data Mining.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Stochastic Models in Space and Time I ST3453

    5 (Michaelmas Term) Total Lecture hours: 36. Some of these lecture hours may be tutorials or computer labs. ST2351 and ST2352 Examination; coursework Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    Students will have ability to discuss and model simple versions of the following processes in time:
    • Everyday examples of stochastic processes
    • Understand and apply the Markov property
    • Describe long run properties of Markov processes
    • Deal with simple Markov processes in discrete time, continuous time and space
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Modern Statistical Methods II ST3456

    5 (Michaelmas Term) Total Lecture hours: 33 Familiarity with basic concepts in probability and statistics. Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    Students will have ability: to devise suitable simulation methods for generating random numbers from a given probability distribution to use the sampled random numbers in order to estimate quantities of interest or evaluate integrals to assess the quality of the generated sample via diagnostic tools.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Microprocessor Systems 1 CS3D1

    5 (Michaelmas Term) 3 Lectures; 1 tutorials; 2 labs N/A Continuous assessment; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    Specific topics addressed in this module include: Number systems; Memory and data representation; Binary arithmetic and logical operations; Floating-point representations and arithmetic; Basic computer architecture; Assembly language and machine code; Flow control; Memory load/store operations and addressing modes.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Data Structures and Algorithms CS3D5a

    5 (Michaelmas Term) 3 Lectures; Lectures/week: 0. Lab/week: 3. Tutorial/week: 1. N/A Coursework Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

  • 1. Undertake software design and construction as members of teams of various sizes.
  • 2. Learn how to choose, learn, and use new languages, tools, and techniques.
  • 3. Gather requirements and develop a problem specification.
  • 4. Examine problem specification and devise an object-oriented solution.
  • 5. Plan implementation of the program taking into account time and team management.
  • 6. Implement a program of reasonable complexity in the Java language.
  • 7. Document the project using standard techniques.
  • 8. Test the solution using standard techniques.
  • 9. Present their work to their peers and their clients.
  • Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    e-Business I CS3BC1

    5 (Michaelmas Term) 3 Lectures. N/A Coursework, examinations Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    Students learn to apply techniques and technologies in support of electronic business and electronic commerce across a range of market sectors and functional areas. Business drivers and alternative models are explored from a management perspective. The business cycle and related issues such as marketing, security, ethical and legal considerations and payment processing options are explored in local, national and international contexts.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Fuzzy Logic CS4001

    5 (Michaelmas Term) 2 lectures; 1 tutorial N/A Coursework, examinations Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

  • At the successful completion of the module the student will have the knowledge of: The inherent imprecision and uncertainty in data and (scientific) concepts; The existence of fuzzy heuristics used in the control of ‘real-world’ system; The new paradigm of computing-with-words;
  • The knowledge will help the students to design and build: Fuzzy-logic based systems; Fuzzy-control systems; Neuro-fuzzy learning systems.
  • Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Formal Verification CS4004

    5 (Michaelmas Term) Total 33 hours(lectures and tutorials) TBC Coursework, examinations Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    Specification languages and logics; axiomatic program semantics, formal proof systems to verify software and system properties such as propositional, predicate and Hoare logic, proofs by mathematical, structural, and rule induction. correctness proofs of functional and imperative programs.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Topics in Functional Programming CS4012

    5 (Michaelmas Term) 2 lectures; Lecture hours: 22. Tutorial hours: 11. Total hours: 33 CS3016 Functional Programming Coursework, examinations Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This course builds on CS3012 which introduced the fundamental concepts of functional programming. In CS4012 we will take an in-depth look at more advanced topics in functional programming and discuss some current research directions in the field.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Computer Graphics CS4052

    5 (Michaelmas Term) 2 lectures; 1 tutorial C or C++ programming, freshman mathematics Coursework, examinations Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    Computer Graphics is an introductory level course covering aspects of: graphics hardware; modeling and object representation; 2D/3D systems and transformations; rendering (visibility, lighting, shading, shadows, texturing, ray tracing); animation (traditional keyframed, motion capture, physically based); and selected hot research topics in the field.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Information Systems Management CS4103

    10 (Michaelmas Term) 3 lectures (Evening Lectures) CS3103 – Business, Management and IT AND CS3104 – Information Systems Strategy Examination; Continuous assessment. Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This module is concerned with management of the IS function and infrastructure in its broadest sense, encompassing data centres, end-user computing, outsourcing and facilities management. Through participative case studies, students gain experience of management issues concerning the full range of IS activities.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Advanced Computer Architecture CS4021

    5 (Michaelmas Term) 2 lectures; 1 tutorial CS3021 Coursework; Examination. Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This course allows students (1) to obtain theoretical and practical experience of concurrent programming without locks (2) to understand the basics of system virtualisation and to obtain practical experience benchmarking and programming virtualised systems (3) to provide students with the knowledge and hands on experience to develop applications software for processors with massively parallel computing resources such as the current generation of GPUs and processors that have the ability to complete more than 64 arithmetic operations per clock cycle and (4) to expand their horizons regarding computer architecture.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Human Factors S4051

    5 (Michaelmas Term) 2 lectures; 1 tutorial N/A Coursework; Examination. Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    The purpose of the module is to give students an understanding of usability problems in interactive system design, the reasons (cognitive and otherwise) underlying these problems and the methods which have been developed to address these issues within systems development.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Computer Vision CS4053

    5 (Michaelmas Term) 2 lectures; 1 tutorial A working knowledge of C+/p> Coursework; Examination. Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    Image preprocessing (such as image enhancement), segmentation of images (e.g. identifying people in a video sequence), representation of shape (so that we can start reasoning about the objects in an image), object recognition (as we'd like to know what we are looking at), 3D vision (i.e. understanding the world in 3 dimensions even though we only have 2 dimensional images), and more. There are just too many techniques to do them all so instead we focus on particular problems and look at the techniques which would be needed to solve those problems.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Knowledge Representation and Automata CS4060

    5 (Michaelmas Term) Total 43 hours (22 lecture, 10 lab, 11 tutorial) Programming competence e.g. CS3011) Coursework; Examination. Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    Knowledge Representation, Description Logics, Finite-state methods, Reasoning about change
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Entrepreneurship CS4081

    5 (Michaelmas Term) 3 lectures N/A) Class participation; Individual & Team assignment Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    Entrepreneurship & High-Tech Venture Creation.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Information Security CS4101

    10 (Michaelmas Term) 3 lecturers (Evening Lectures) N/A) Examination; Continuous assessment. Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This module gives students a comprehensive appreciation of information systems security concepts and techniques including privacy and data protection. Students learn how to factor security considerations into their professional practice, and practise useful techniques such as risk analysis and contingency planning
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Information Systems Development III CS4106

    10 (Michaelmas Term) 3 lecturers (Evening Lectures) N/A) Examination; Continuous assessment. Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This module gives students a comprehensive appreciation of information systems security concepts and techniques including privacy and data protection. Students learn how to factor security considerations into their professional practice, and practise useful techniques such as risk analysis and contingency planning
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Decision Analysis ST4005

    5 (Michaelmas Term) 3 lectures hours N/A Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This course will look into some of the topics covered in the earlier management science courses at greater depth, with emphasis on how the methods can be practically implemented, principally through Excel.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Advanced Computational Linguistics CS4LL5

    5 (Michaelmas Term) Total: Lecture hours: 22. Lab hours: 6. Tutorial hours: 5 No pre-requisite: to implement and experiment with tools will need to be able to program in C+/p> Examination; Coursework Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    The aim is to give a grounding in so-called unsupervised machine learning techniques which are vital to many language-processing technologies including Machine Translation, Speech Recognition and Topic Modelling. Whilst studied in these contexts, the techniques themselves are used much more widely in data mining and machine vision for example.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Programming Project I CS1013

    5 (Hilary Term) 3 lectures CS1010 Project Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    CS1013 is a course which concentrates on development of practical programming ability through example-based lecturing coupled with intensive laboratory sessions. The emphasis throughout is on producing working programs, starting with interactive graphical applications and moving on to construction of a larger group project involving a data visualisation task.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Introduction to Computing II CS1022

    5 (Hilary Term) 2 lectures; 1 tutorial; 2 laboratory hours CS1021 Coursework; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This module continues directly from CS1021 (which is a prerequisite) and examines the structure and behaviour of computer systems in greater depth. In particular, this module introduces students to the implementation of simple data structures (stacks, multi-dimensional arrays, composite data types), subroutines, exceptions, interrupts and basic I/O at the machine level.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Telecommunications I CS1031

    5 (Hilary Term) 2 lectures; 1 tutorial N/A Coursework; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    Overview of networks, their topologies and how they are categorised.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Computer Engineering I CS1E03

    5 (Hilary Term) 3 lectures; 2 labs N/A Continuous assessment; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This module aims to equip students with the skills to design and develop simple imperative programs. It provides a solid grounding in algorithm design and programming techniques, in preparation for later courses that require programming.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Introduction to Statistics II ST1252

    5 (Hilary Term) 2 lectures; 1 tutorial ST1251 Continuous assessment; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    To introduce students to the elementary ideas of statistical inference and the use of simple statistical methods in practical situations.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Programming Project II CS2013

    5 (Hilary Term) 2 lectures; N/A Continuous assessment; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    Project teams will be created, to make a "software product" for a client within the Department. Guidance given on software design, work distribution and project planning but decisions are the responsibility of the team.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Concurrent Systems and Operating Systems CS2016

    5 (Hilary Term) 3 lectures; 1 laboratory hour Working knowledge of C/C+ and an understanding of Computer Organization Coursework; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    The first part of this module introduces students to concurrency and concurrent programming. The aim is to provide students with the ability to develop concurrent software systems using standard techniques and constructs. The second part of the module addresses various aspects of the design of modern operating systems
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Microprocessor Systems CS2021

    5 (Hilary Term)3 lectures; 1 tutorial; 3 laboratory hours CS1021/22 (Introduction to Computing), CS1026 (Digital Logic Design), CS1025 (Electrotechnology). Coursework; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This module provides an introduction to the MC68008 microprocessor including clock and reset circuitry design, memory-map design, serial I/O design, system exceptions and interrupts as well as system monitor design and implementation. An introduction to hardware description languages, reconfigurable hardware systems and schematic design is also provided through the use of industry standard design tools.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Computer Architecture I CS2022

    5 (Hilary Term)2 lectures; 2 laboratory hours None Coursework; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    The aims of the course are to learn register-transfer specification and design and learn the fundamentals of an instruction processor.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Applied Probability 2 ST2005

    5 (Hilary Term)2 lectures; 1 tutorial ST1002, ST2004 Examination; Continuous assessment Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This module will develop several important ideas in statistical analysis making use of some of the ideas introduced in ST2004. It acts as a bridge to the sophister years by introducing the fundamental ideas that are used in the more advanced statistics modules that will take place then.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Engineering design IV: Project CS2E10

    10 (Hilary Term)Total: 55 hours (lectures and labs) Coursework Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    The 2E10 Engineering Design IV Module introduces the challenge of electronic systems design. The project is an example of ‘hardware and software co-design’ and the scale of the task is such that it requires teamwork and a co-ordinated effort. Each group has access to the basic shell of a vehicle that includes the motor assemblies, battery holders and sensors. The completed system should comprise of a computer controlled autonomous vehicle with motor driven wheels and position sensors. The motors and the position sensors should operate under control from a programmable microcontroller and the vehicle should communicate with a base station using a wireless standard module.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Systems Analysis and Design 1 CS2BC1

    5 (Hilary Term)4 lectures Coursework; examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This module introduces students to the theory and practice of designing, creating and maintaining large software systems within demanding and changing business environments. Modern enterprises are critically reliant on information systems to support their business needs. The module covers the standard business and engineering processes, approaches and disciplines applied in industry today.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Statistical Analysis 3 ST3002

    5 (Hilary Term)2 lectures; 1 laboratory hour Engineering Mathematics III, Applied Statistics and Applied Probability. Coursework; examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    Binomial, Poisson, Multinomial distributions, Model based methods, Graphical techniques.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Research Methods ST3004

    5 (Hilary Term) 3 lectures; N/A Coursework; examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    Upon completing this course, students should have an understanding of the nature of the research process, drawing upon primary and secondary data sources; be able to locate, analyse and interpret quantitative and qualitative data; and to present the findings.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    e-Business II CS3BC2

    5 (Hilary Term) 3 lectures; A basic understanding of XML and SQL and of Java programming. Coursework; examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This module aims to provide an understanding of modern, web based approaches for developing software applications, services and data structures for e-business applications. It addresses the standards, practical tools and techniques of web-based, e-business application development, including 3-tier application server architectures, web services, workflow and service composition, web content and meta-data using HTML and XML
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Software Design and Implementation CS3D5b

    5 (Hilary Term) Lab/week: 3; Tutorial/week: 1 N/A Coursework; Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

  • 1. Undertake software design and construction as members of teams of various sizes.
  • 2. Learn how to choose, learn, and use new languages, tools, and techniques.
  • 3. Gather requirements and develop a problem specification.
  • 4. Examine problem specification and devise an object-oriented solution.
  • 5. Plan implementation of the program taking into account time and team management.
  • 6. Implement a program of reasonable complexity in the Java language.
  • 7. Document the project using standard techniques.
  • 8. Test the solution using standard techniques.
  • Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Computer Networks CS3D3

    5 (Hilary Term) 3 Lectures; 4 labs N/A Continuous assessment; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This module introduces students to computer networks and concentrates on building a firm foundation for understanding Data Communications and Computer Networks. It is based around the OSI Reference Model, which deals with the major issues in the bottom four (Physical, Data Link, Network and Transport) layers of the model. Students are also introduced to the areas of Network Security and Mobile Communications. This module provides the student with fundamental knowledge of the various aspects of computer networking and enables students to appreciate recent developments in the area.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Artificial Intelligence I CS3061

    5 (Hilary Term)2 lectures; 1 tutorial CS3011 Continuous assessment; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    An introduction to AI.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Advanced Telecommunications CS3031

    5 (Hilary Term)2 lectures; 1 tutorial CS2031 – Telecommunications II Coursework; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This option concentrates on building upon the students JF and SF years knowledge and introduces them to advanced topics in the area of data communications and telecoms.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Concurrent Systems I CS3014

    5 (Hilary Term)3 lectures CS2014, CS2015. A good knowledge of C programming Module Co-requisite: CS3021 Coursework; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    The architecture and programming of modern parallel computing systems. The particular emphasis of this part of the course is architecture, and different ways to achieve speedup of programs using parallelism.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Statistical Methods for Computer Science ST3009

    5 (Hilary Term)Lecture: 2 hours per week. Labs: 1 hour per week. Total: 33 hours N/A Coursework; Examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    The module provides an introduction to statistics and probability for computer scientists. The aim is to provide the basic grounding needed for machine learning and algorithm performance analysis.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Software Engineering Group Project CS3013

    5 (Hilary Term)2 Lectures N/A Coursework; Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This course follows on from CS3012(Software Engineering) and aims to give students a deeper understanding of software engineering concepts and tools through practical application. This takes the form of a large "hands-on" group project that covers numerous aspects of building object-oriented software systems including problem analysis, usage of development environments, project management, team management, design, implementation, testing and documentation. Students will take a leadership role within these groups which are combined with students taking course CS2013.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Computational Mathematics CS3081

    5 (Hilary Term)2 Lectures; 1 tutorial N/A Coursework; examination Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    The aim of the module is to teach, in sufficient detail for practical implementation, the mathematical concepts and methods appropriate to writing computer programs for science and engineering applications in general, and in particular: computer graphics, computer vision, image processing, robotics, physical simulation, and control.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Information Systems ST3005

    5 (Hilary Term)3 lectures N/A Examination; Continuous assessment Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    The objective of this course is to introduce students to information systems in business and examines how management information and decision support systems can support improved organisational performance. Information security and control surrounding these systems and aspects of ethical use of IT are also covered..
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Stochastic Modules in Space and Time II ST3454

    5 (Hilary Term)Total Lecture hours: 33 Solid knowledge in mathematics and statistics required e.g. on Linear algebra, Integration and differentiation, expectation operator Examination; Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    When students have successfully completed this module they should be able to: Define, describe and apply the different methods introduced in the course Program and analyse a dataset with these methods. Interpret the outputs of the data analysis performed by a computer statistics package
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Modern Statistical Methods I ST3455

    5 (Hilary Term)Total 33 Lab hours ST2351, ST2352 Examination; Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This module will describe several topics of a more advanced nature in probability modelling and statistics.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Knowledge Engineering CS4D2B

    5 (Hilary Term)2 Lectures; 1 Tutorial N/A Examination; Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    On completion of this module the student will be able to:
  • 1. Design and engineer a Database Management System (DBMS) with consideration given to hardware, information organization, hashing and indexing.
  • 2. Understand the structure of, and apply advanced manipulation techniques to, XML documents.
  • 3. Develop skills in managing knowledge using Ontological and Semantic Web technologies.
  • 4. Design and develop Ontologies
  • 5. Understand and Compare different Information Retrieval techniques, specifically those used on the web.
  • Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Digital Logic Design CS1026

    10 (Full Year Module Michaelmas & Hilary Terms)2 lectures; 1 tutorial; 2 laboratory hours N/A Coursework; Examination; Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    Switching algebra; Boolean functions; minimisation; arithmetic and other logic; asynchronous sequential logic; latches; gated latches. Flip-flops; synchronous sequential logic; finite state machines; algorithmic state machines; control paths; data paths; counters & sequencers.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Mathematics CS1003

    10 (Full Year Module Michaelmas & Hilary Terms)2 lectures; 1 tutorial; N/A Coursework; Examination; Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This module aims to develop the students’ skills and abilities in the mathematical methods necessary for solving practical problems. In the first semester students will encounter some of the key mathematical structures at the heart of computer science including the representation of data using matrices. They will gain a greater appreciation of the relationships between calculus and the graphs of functions, including the representation of functions using Taylor Series. During Semester 2 students will be introduced to discrete mathematics and mathematical logic along with their applications to computer science. In particular, the module will introduce set operations, discrete maths functions in Number Theory and Logic calculation. This part of the module is influenced by the approaches of Backhouse, Dijkstra and Gries
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Introduction to Programming CS1010

    10 (Full Year Module Michaelmas & Hilary Terms)2 lectures; 1 tutorial; 4 laboratory hours N/A Coursework; Examination; Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This module provides an introductory course in computer programming. The modules take a practical approach to teaching the fundamental concepts of computer programming with a strong emphasis on tutorial and laboratory work and are an important vehicle for developing student’s analytical and problem-solving skills. The modules aim to give students an understanding of how computers can be employed to solve real-world problems. Specifically, the modules introduce students to the object-oriented approach to program design and teach them how to write programs in an object-oriented language (in this case Java).
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Programming Techniques CS2010

    10 (Full Year Module Michaelmas & Hilary Terms)2 lectures; 1 tutorial; 3 laboratory hours An introductory course on programming; CS1010 Coursework; Examination; Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This is a practical course that provides students with a solid grounding in programming using object orientation. Object Orientation, Design By Contract, Algorithms and Abstract Data Types.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Intermediate Programming and Natural Language Processing CS2LL3

    10 (Full Year Module Michaelmas & Hilary Terms)2 lectures; 1 tutorial; 1 laboratory hour None, though some prior experience of programming a definite advantage, and is something participants from the CSLL degree will have from their first year Java course Coursework; Examination; Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    C++, parsing, finite state techniques, statistical linguistics.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Software Applications 2 ST2001

    10 (Full Year Module Michaelmas & Hilary Terms)2 laboratory hours None, though some prior experience of programming a definite advantage, and is something participants from the CSLL degree will have from their first year Java course Coursework; Examination; Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    The purpose of this course is to give students experience in advanced computer applications. This will include the advanced applications of Excel. The course will introduce students to database technology using Microsoft Access. Students will use Visual Basic for Applications (MS Office 2010). This course is a computer laboratory based course. Students are given notes that encourage self-paced learning. Interaction with the course instructor and peers is encouraged.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Management Science Methods ST2006

    10 (Full Year Module Michaelmas & Hilary Terms)2 lectures; 1 tutorial ST1004 Coursework; Examination; Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

  • Semester 1 - Formulate and solve Linear and Goal Programming problems using the Simplex Method, Perform Sensitivity Analysis on the output from a Linear and Goal Programming problem, Formulate and solve Transportation, Transhipment and Assignment problems, Formulate a 0 – 1 Linear Programming problem and solve using the Cutting Plane and Branch and Bound Methods, Analyse networks for the Chinese Postman and Travelling Salesman Problems, Other relevant mathematical models
  • Semester 2 - Specific topics addressed in this module include: Entities, attributes and variables; Events; Resources; Queues;Steady-state models and transients; Software for simulation; Statistical analysis of output;
  • Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Software Applications 3 ST3001

    10 (Full Year Module Michaelmas & Hilary Terms)2 lectures; ST1001 – Software Applications I and ST2001 – Software Applications II Coursework; Examination; Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This course will introduce students to Visual Basic programming and students will use Visual Basic 2012 to learn how to build small software applications. The course will also give students experience in client server database technologies. This course will be based on various databases such as MySQL and Microsoft Access. The course will introduce students to writing database queries using SQL. HTML and PHP will be used to develop user front ends to these databases. This course is a computer laboratory based course. Students are given notes that encourage self-paced learning. Interaction with the course instructor and peers is encouraged.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Advanced Information Systems CS4104

    10 (Full Year Module Michaelmas & Hilary Terms)3 lectures (Evening Lectures) N/A Examination; Continuous assessment. Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    Through presentations by external and internal experts, online seminars and workshops, this module addresses pressing contemporary topics in IT and IS including practitioner-oriented themes and cutting-edge research results. Students investigate, discuss, present and write about topics that interest them.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Innovation CS4102

    10 (Full Year Module Michaelmas & Hilary Terms)1.5 lectures (Evening Lectures) N/A Examination; Continuous assessment. Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    This course is designed to enable students to handle innovation ("getting new things done") in business effectively, with special emphasis on creativity, the use of intellectual property and the special problems of raising seed and venture capital for innovative projects and of protecting investment in innovation.
    Module Code and Module Name ECTs credits Duration and Semester Prerequisite Subjects Assessment Contact

    Strategic Information Systems ST4500

    10 (Full Year Module Michaelmas & Hilary Terms)4-6 lectures, 1 laboratory hour Information Systems and Technology or equivalent MSISS, EM and CS&B students (10 ECTS). A 3,000 word essay (25%); a literature review (25%); a three hour end of year examination (50%). Dr Inmaculada Arnedillo-Sánchez , School of Computer Science & Statistics

    Course Description and Learning Outcomes

    To present students with an overview of the business and social impacts of current developments in information systems (IS) and ICT. To equip students to think critically about these impacts and their implications for business and society today and in the future.