ObjectivesTo 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 operational research 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 operational researcher. 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 or operations research nature.
Entry requirementsEntry Qualifications BSc degree, of Upper Second class standard or above, in Mathematics or a related subject (or an equivalent qualification). Statistics and Operational Research should form a minor component of the degree. Language requirements: IELTS 6.0 or TOEFL 540 (200) or comparable.
Academic titleMSc Statistics and Operational Research
Course description
Course Description
The MSc in Statistics and Operational Research is aimed at students with a first degree in which the major subject was mathematics although some prior knowledge of statistics (eg significance testing and basic statistical distributions) and operational research (eg linear programming) is expected.
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
BIOLOGICAL SIGNAL ANALYSIS
Compulsory: COMBINATORIAL OPTIMISATION
Compulsory: EXPERIMENTAL DESIGN
Compulsory: LINEAR MODELS
Compulsory: NONLINEAR PROGRAMMING
Compulsory: STOCHASTIC PROCESSES
Core: DISSERTATION
Core: RESEARCH METHODS
GRAPH THEORY
HEURISTIC AND EVOLUTIONARY COMPUTATION
MACHINE LEARNING AND DATA MINING
MATHEMATICS OF PORTFOLIOS
PANEL DATA METHODS
TIME SERIES ECONOMETRICS
Teaching and Assessment Methods
A: Knowledge and Understanding
Learning Outcomes
A1 : A range of ideas concerning Statistics and Operational Research including methods appropriate in specialized applications.
A2 : Ways in which statistical methods can aid understanding in the social sciences.
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 Operational Research, including an awareness of the development of these areas of research.
Teaching Methods
A1-A4 are principally acquired through the coherent programmes of lectures, exercises and problem classes. These are supplemented, where appropriate, by 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 thesis on specialized topics. During the production of their written work, students are expected to extend and enhance the basic course material concerning 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 project.
Assessment Methods
The level of attainment of these skills is assessed through the summer examinations, and through examination of the summer project.
C: Practical Skills
Learning Outcomes
C1 : Carry out analyses of complex data sets, design experiments & analyse practical OR 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 exercises and computer work.
C4-C7 are developed during the course of the preparation of the essay and the thesis.
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 project
C4-C7 are assessed through the essay and summer project
D: Key Skills
Learning Outcomes
D1 : Write clearly and effectively
D2 : Use computer packages and/or programming languages for data analysis and computation and use computer packages for presentation of material to others.
D3 : Enhance existing numerical ability
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 and thesis work, and by the class teachers' feedback on written solutions to problems.
D2 results from the coursework associated with various lecture courses.
D3 is a natural consequence of courses with high numeric content.
D4 is a consequence of the coursework, problems classes, lectures and laboratory work.
D5-6 result 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 project.