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MSc Statistics and Computer Science

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  • Objectives
    To enhance the general skills of students (including IT skills, presentation skills, problem solving abilities, numeracy and their ability to retrieve information in an efficient manner.) To offer students the opportunity to study statistics and computer science to an advanced level within an environment informed by current research. To provide students with advanced training that will be of use in a career as a statistician or computer scientist. To provide students with training in the preparation of reports involving mathematical material, including correct referencing, appropriate layout and style. To provide students with a research-type experience that will aid them in their approach to further research activity. To provide students with information that will help them to make an informed judgement as to the appropriate methods to employ when analysing a problem of a statistical nature.
  • Entry requirements
    Entry Qualifications BSc degree, of Upper Second class standard or above, in Mathematics or a related subject (or an equivalent qualification). Statistics should form a minor component of the degree. Knowledge of a computer programming language would be an advantage, but is not essential. Language requirements: IELTS 6.0 or TOEFL 540 (200) or comparable.
  • Academic Title
    MSc Statistics and Computer Science
  • Course description

    Course Description
    The MSc in Statistics and Computer Science is suitable for mathematically-trained candidates who wish to develop expertise in aspects of computer science.

    Modules and Options

    The lists of modules below represent the range of options available for each year of study. This may not be a complete list of the options you will study, and may be subject to change, so please contact the department for further details.

    Stage 1

        ADVANCED RELATIONAL AND OBJECT-ORIENTED DATABASES
        APPLICATIONS OF DATA ANALYSIS
        ARTIFICIAL NEURAL NETWORKS
        BIOLOGICAL SIGNAL ANALYSIS
        Compulsory: EXPERIMENTAL DESIGN
        Compulsory: LINEAR MODELS
        Compulsory: MATHEMATICAL RESEARCH TECHNIQUES USING MATLAB
        Compulsory: STOCHASTIC PROCESSES
        Core: DISSERTATION
        Core: RESEARCH METHODS
        GRAPH THEORY
        HEURISTIC AND EVOLUTIONARY COMPUTATION
        MACHINE LEARNING AND DATA MINING
        NETWORKS: PROTOCOLS AND SECURITY
        OBJECT ORIENTED SOFTWARE DESIGN
        PANEL DATA METHODS
        PROGRAMMING WITH C# (DOUBLE MODULE)

    Teaching and Assessment Methods

    A: Knowledge and Understanding
        Learning Outcomes
        A1 : A range of ideas concerning Statistics and Computer Science including methods appropriate in specialized applications.
        A2 : Ways in which statistical methods can aid understanding in application areas.
        A3 : Some of the limitations and assumptions underlying standard methods.
        A4 : The fact that apparently disparate methods may interconnect.
        A5 : One or more current areas of research in Statistics or Computer Science, including an awareness of the development of these areas of research.

        Teaching Methods
        A1-A4 are principally acquired through the coherent programmes of lectures, problems and problem classes. These are supplemented by problems requiring, where appropriate, the use of computers, computer packages, textbooks, handouts and on-line material.

        In most courses there is regular set work. This work is marked and this process informs the course teacher of common difficulties that require extra attention during the subsequent problem classes.

        A5 is principally acquired through the preparation of an essay and a dissertation on specialized topics. During the production of their written work, students are expected to extend and enhance the basic course material on internet searching and the production of mathematical texts. The research guidance during the summer is a critical aspect of this training.

        Assessment Methods
        Knowledge and understanding are assessed through examinations, essays and the summer dissertation.

    B: Intellectual/Cognitive Skills
        Learning Outcomes
        B1 : Analyse a mass of information and carry out an appropriate analysis.
        B2 : Express a problem in mathematical terms and carry out an appropriate analysis.
        B3 : Reason critically and interpret information in a manner that can be communicated effectively to non-specialists.
        B4 : Integrate and link information across course components.
        B5 : Under guidance of a supervisor, plan and carry out a piece of research and present the results in a coherent fashion.

        Teaching Methods
        B1-3 These skills are developed through the regular coursework exercises. In seeking to answer these exercises students become accustomed to identifying key facts in a body of information. The problems classes provide back-up as required.

        B4-5 These skills are initiated during the course of the preparation of the essay and are further developed during the course of the summer dissertation.

        Assessment Methods
        The level of attainment of these skills is assessed through the summer examinations, and through examination of the summer dissertation.

    C: Practical Skills
        Learning Outcomes
        C1 : Carry out analyses of complex data sets, design experiments & analyse practical statistical problems.
        C2 : Use simple algorithms.
        C3 : Use computer programmes and/or packages.
        C4 : Use a mathematical word-processing package.
        C5 : Make an effective literature search.
        C6 : Prepare a technical report.
        C7 : Give a presentation and defend their ideas in an interview.

        Teaching Methods
        C1-C3 are developed through the programme of lectures, regular problems and computer work, particularly in MA308 and MA310.
        C4-C7 are developed during the course of the preparation

        Assessment Methods
        C1-C2 are assessed by the regular coursework and examinations.
        C3 is assessed in this way and also by any computer output that forms part of the summer dissertation
        C4-C7 are assessed through the essay and summer dissertation

    D: Key Skills
        Learning Outcomes
        D1 : Write clearly and effectively
        D2 : Use computer packages and/or programming languages for data analysis and computation and for presentation of material to others.
        D3 : Enhance existing numerical ability, including in particular the ability to carry out a statistical analysis.
        D4 : Choose the appropriate method of inquiry in order to address a range of practical and theoretical problems.
        D5 : Learn from feedback and respond appropriately and effectively to supervision and guidance.
        D6 : Work pragmatically to meet deadlines.

        Teaching Methods
        D1 is promoted by the supervisor of the essay.

        D2 results from the coursework associated with various lecture courses, and is associated with the production of the essay.

        D4 is a natural consequence of courses with high numeric content.

        D5 is a consequence of the coursework, problems classes, lectures and laboratory work.

        D6 results from a tightly timetabled course of lectures and submission dates that require the student to effectively organise time to meet deadlines.

        Assessment Methods
        Key skills are assessed throughout the degree via coursework, examinations, the essay and the summer dissertation.

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