| Code | Faculty |
|---|---|
| 07250343 | Faculty of Economic and Management Sciences |
| Credits | Duration |
|---|---|
| Minimum duration of study: 1 year | Total credits: 180 |
| Prof IN Fabris-Rotelli [email protected] | +27 (0)124205420 |
As long as progress is satisfactory, renewal of registration of a master’s student will be accepted for a second year of study in the case of a full-time student. Renewal of registration for a third and subsequent years for a full-time student will only take place when Student Administration of the Faculty receives a written motivation (the required form can be obtained from the Head of Department) that is supported by the Head of Department and Postgraduate Studies Committee.(See Regulations G.32 and G.36.)
Dissertations/mini-dissertations, curricula and modules
Article for publication
A dean may require, before or on submission of a dissertation/mini-dissertation, the submission of a draft article for publication to the supervisor. The draft article should be based on the research that the student has conducted for the dissertation/mini-dissertation and be approved by the supervisor concerned. The supervisor should then have the opportunity to take the paper through all the processes of revision and resubmission as may be necessary and/or appropriate in order to achieve publication.
Submission of dissertation/mini-dissertation
A dissertation/mini-dissertation is submitted to the Head: Student Administration/Departmental Postgraduate Office, before the closing date for the various graduation ceremonies as announced annually.
For examination purposes, a student must, in consultation with the supervisor, submit a sufficient number of bound copies and/or e-copies of the dissertation/mini-dissertation, printed on good quality paper and of good letter quality, to the Head: Student Administration/Departmental Postgraduate Office. Permission to submit the dissertation/mini-dissertation in unbound form may be obtained from the supervisor concerned on condition that a copy of the final approved dissertation/mini-dissertation is presented to the examiners in bound format or electronic format.
In addition to the copies already mentioned, each successful student must submit a bound paper copy as well as two electronic copies of the approved dissertation/mini-dissertation to the Head: Student Administration/Departmental Postgraduate Office in the format specified by the faculty and in accordance with the minimum standards set by the Department of Library Services, before 15 February for the Autumn graduation ceremonies and before 15 July for the Spring graduation ceremonies, failing which the degree will only be conferred during a subsequent series of graduation ceremonies.
This degree is phased out and will not be offered as from 2019.
Module content:
Supervised and unsupervised methods, including computational methods, within the broader context of data mining. Supervised learning. Linear methods for Regression, Classification and Prediction. Basis Expansions, Regularisation, Smoothing, Additive models and Support Vector Machines.
Unsupervised learning: Clustering, principal components, dimensional reduction. Data methods: Organisation of data and exploratory data analysis.
Module content:
The module is primarily an article based on and covers the most recent literature that discusses the developments and research in, for example, Shewhart charts, Exponentially Weighted Moving Average (EWMA) charts, Cumulative Sum (CUSUM) charts, Q-charts, Parametric and Nonparametric charts, Univariate and Multivariate charts, Phase I and Phase II control charts, profile monitoring and other research topics.
Module content:
A compulsory bootcamp must be attended as part of this module – usually presented during the last week of January each year. Details regarding the venue and specific dates are made available by the department each year. The bootcamp will cover the basics of research to prepare students for the research component of their degree. Students can be exempt from the bootcamp if it was already attended in a previous year or for a previous degree. Each year of registration for the master’s degree will also require the attendance of three departmental seminars. Students should ensure that their attendance is recorded by the postgraduate co-ordinator present at the seminars. The department approves the seminars attended. Students are also required to present their mini-dissertation research proposal within the department or at a conference.
Module content:
Difference equations. Lag operators. Stationary ARMA processes. Maximum likelihood estimation. Spectral analysis. Vector processes. Non-stationary time series. Long-memory processes.
Module content:
Regression introduction: Simple and multiple regression. Multicollinearity, Heteroscedasticity, Ridge regression. Logistic regression: Estimation, inference and applications. Non Linear regression: Estimation, inference and applications. Text mining: Topic modelling with applications. Survival regression: Survival models applied in regression. Regression extensions: CART, MARS and Conjoint analysis.
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