Course description
Course Description
The MSc in Statistics and Data Analysis focuses on the ability to analyse data, which is fundamental to quantitative social science. Some modules on this course are also given by experts from the Institute for Social and Economic Research.
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
APPLICATIONS OF DATA ANALYSIS
Compulsory: ECONOMETRIC METHODS AND APPLICATIONS
Compulsory: EXPERIMENTAL DESIGN
Compulsory: LINEAR MODELS
Compulsory: STOCHASTIC PROCESSES
Core: DISSERTATION
Core: RESEARCH METHODS
GAME THEORY AND APPLICATIONS
MACHINE LEARNING AND DATA MINING
MATHEMATICS OF PORTFOLIOS
NONLINEAR PROGRAMMING
PANEL DATA METHODS
TIME SERIES ECONOMETRICS
Teaching and Assessment Methods
A: Knowledge and Understanding
Learning Outcomes
A1 : A range of ideas concerning Statistics and Data Analysis including methods appropriate in specialized applications.
A2 : Ways in which statistical methods can aid understanding in the social sciences, particularly economics.
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 Data Analysis, 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 computer programmes and/or packages.
C3 : Use a mathematical word-processing package.
C4 : Make an effective literature search.
C5 : Prepare a technical report.
C6 : Give a presentation and defend their ideas in an interview.
Teaching Methods
C1-C2 are developed through the programme of lectures, regular exercises and computer work.
C3-C6 are developed during the course of the preparation of the essay and the thesis.
Assessment Methods
C1 is assessed by the regular coursework and examinations.
C2 is assessed in this way and also by any computer output that forms part of the summer dissertation.
C3-C6 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.
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 dissertation work and by 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 dissertation.
