Department of Statistics and Actuarial Science
This Department is one of the oldest of its kind in South Africa. Ten years before it was founded in 1946, Statistics was already presented as a subject, but mainly for students in the commercial sciences. Because of this, the Department was established in the Faculty of Economic and Management Sciences. Since then the number of environments needing tuition in statistics has increased rapidly. Since 1985, actuarial science has also been offered by the Department. Besides its academic responsibilities, the Department also houses the Centre for Statistical Consultation. The Centre provides a statistics support service to all the researchers of the University.
areas of specialisation include -
The Department offers tuition in various related areas: actuarial science (up to doctoral level), financial risk management (up to master's level), statistics (up to doctoral level), and mathematical statistics (up to doctoral level). Listed below is a summary of the fields covered.
The postgraduate programmes cover certain of the later subjects of the Actuarial Society of South Africa (and the Institute and Faculty of Actuaries in the UK). In the Honours programme Financial Economics (A205), Actuarial Risk Management (A301) and Communications (A302) are covered. The Postgraduate Diploma and Master's programmes cover subjects on the Fellowship level, with the Master's programme also requiring the submission of a research project/thesis. Successful students are able to obtain exemptions (from the examinations of the actuarial profession) for not only the Core Technical subjects, but also for the subjects in the Core Principles and Fellowship Principles subjects.
Financial Risk Management
Financial mathematical statistics, advanced financial derivatives, financial risk management, advanced modern portfolio management theory, practical financial modelling (Excel and Visual Basic), credit derivatives, alternative investments, stochastic financial simulation and financial risk management programming (R/S Plus, SAS Risk Dimensions, Mathlab).
Mathematical Statistics and Statistics
Advanced statistical inference, Bayesian statistics, biostatistics, bootstrap and related computer-intensive techniques, categorical data analysis, consultation practice, data mining, experimental design, extreme value theory, large sample analysis, multidimensional scaling, multivariate statistical analysis, probability models and stochastic simulation, probability theory, non-parametric techniques, sampling theory, S-PLUS and R programming, statistical learning theory, statistical quality control, survival analysis, time series analysis.