Yearbooks

Programme: BComHons Statistics and Data Science

Code NQF level Faculty Duration Credits
07240062 NQF level:  08 Faculty of Economic and Management Sciences Minimum duration of study: 1 year Total credits: 120
Contact:
Dr IN Fabris-Rotelli
[email protected]
+27 (0)124205420

Admission requirements

  • Relevant BCom degree with with an average of at least 65% in Statistics or equivalent on 3rd year level.

Note:

  • Student numbers are limited to a maximum of 40, collectively over all honours programmes in the Department of Statistics.
  • Historical performance during prior studies will also be considered in selecting students. Specific attention will be given to modules repeated and duration of study.
  • A compulsory language proficiency test must be completed at the University of Pretoria. The Departmental Postgraduate Selection Committee will facilitate the test through the university’s language unit. Based on the outcome, a student may be required to do additional language courses.

Examinations and pass requirements

In calculating marks, General Regulation G12.2 applies.
Subject to the provisions of General Regulation G.26, a head of department determines, in consultation with the Dean

  • when the honours examinations in his/her department will take place, provided that:
  1. honours examinations which do not take place before the end of the academic year must take place no later than 18 January of the following year, and all examination results must be submitted to Student Administration by 25 January; and
  2. honours examinations which do not take place before the end of the first semester may take place no later than 15 July, and all examination results must be submitted to Student Administration on or before 18 July.
  • whether a candidate will be admitted to a supplementary examination, provided that a supplementary examination is granted, only once in a maximum of two prescribed semester modules or once in one year module;
  • supplementary examinations (if granted) cover the same subject matter as was the case for the examinations;
  • NB: For the purpose of this provision, the phrase "not sit for an examination more than twice in the same subject" as it appears in General Regulation G.18.2, implies that a candidate may not be admitted to an examination in a module, including a supplementary examination, more than three times.
  • the manner in which research reports are prepared and examined in his/her department.

NB: Full details are published in each department's postgraduate information brochure, which is available from the relevant head of department. The minimum pass mark for a research report is 50%. The provisions regarding pass requirements for dissertations contained in General Regulation G.12.2 apply mutatis mutandis to research reports.

Subject to the provisions of General Regulation G.12.2.1.3, the subminimum required in subdivisions of modules is published in the study guides, which is available from the relevant head of department.

Minimum credits: 120

All honours students in Statistics/Mathematical Statistics should enrol for STK 796 which is a compulsory but non-credit-bearing module. The satisfactory completion of this module is a prerequisite for embarking on the research component of the degree programme.

Select 2 modules from the list of electives.

Core modules

  • Module content:

    The emphasis is on the theoretical understanding and practical application of advances in statistical modelling. The following topics are covered: Single equation models: Nonparametric regression. Bootstrap procedures within regression analysis, k-nearest neighbour classification. Modelling categorical dependent variables - Logit/Probit models. Multiple outputs. Linear regression of an indicator matrix. Ridge regression. Non-linear regression modelling.  Some new developments in regression and classification.
    Simultaneous equation models: Specification, identification and estimation of simultaneous equation models.

    View more

  • Module content:

    Point and Interval estimation. Sampling distributions, central limit theorem, simulations and Bootstrap. Bayesian inference, posterior distribution, Hypotheses testing using confidence intervals, ratio tests, simulated null distributions and power function.

    View more

  • Module content:

    Matrix methods in statistics. Simple and multiple regression models. Sums of squares of linear sets. Generalised t- and F-tests.  Residual analysis. Diagnostics for leverage, influence and multicolinearity. Indicator variables. Regression approach to analysis of variance. Weighted least squares. Theory is combined with practical work.

    View more

  • Module content:

    Refer to the document: Criteria for the research management process and the assessment of the honours essays, available on the web: www.up.ac.za under the Department of Statistics: Postgraduate study.

    View more

  • Module content:

    A compulsory bootcamp must be attended as part of this module – usually presented during the last week of January each year (details are made available by the department ). The bootcamp will cover the basics of research to prepare students for the research component of their degree. The bootcamp should be done in the same year as registration for STK 795/WST 795. Each year of registration for the honours 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. In addition, students are required to present their STK 795/WST 795 research in the department during the year of registration for these modules.

    View more

Elective modules

  • Module content:

    Mixtures of distributions and regressions, frequentist and Bayes estimation. Latent components, soft allocation and belongings. Applications in unstructured data, including text data. Identification and interpretation of behavioural patterns.

    View more

  • Module content:

    This module will cover the core theoretical concepts of macroeconomics focussing specifically on labour and goods markets as well as intertemporal issues, such as capital markets. Topics will include economic growth, exogenous and endogenous, business cycles, monetary economics, stabilization policies and structural policies.

    View more

  • Module content:

    The core concepts of microeconomic theory will be the focus of the module, including: demand and supply, consumer theory, firm theory, markets and market structure, general equilibrium, information economics and behavioural economics. Applications of this theory will feature prominently.

    View more

  • Module content:

    Simple random sampling. Estimation of proportions and sample sizes. Stratified random sampling. Ratio and regression estimators. Systematic and cluster sampling. Introduction to spatial statistics. Spatial sampling – both model and design based approaches.

    View more

  • Module content:

    Quality control and improvement. Shewhart, cumulative sum (CUSUM), exponentially weighted moving average (EWMA) and Q control charts. Univariate and multivariate control charts.  Determining process and measurement systems capability. Parametric and nonparametric (distribution-free) control charts. Constructing control charts using Microsoft Excel and/or SAS. Obtaining run-length characteristics via simulations, the integral equation approach, other approximate methods and the Markov-chain approach.

    View more

  • Module content:

    Efficient programming, Monte Carlo simulation, sampling of discrete and continuous probability models, General transformation methods, Accept-reject methods, Monte Carlo integration, importance sampling, numerical optimisation, Metropolis-Hastings algorithm, GIBBS sampling.

    View more

  • Module content:

    This module considers specific topics from the diverse field of statistics as deemed supportive towards the training of the cohort of scholars.

    View more

  • Module content:

    Specification of linear mixed model, model assumptions, estimation (REML and ML), diagnostics, hypothesis tests, interpretation of parameter estimates, calculating predicted values. Specific models: two- and three-level models for clustered data, intraclass correlation coefficients, repeated measures data, random coefficient models for longitudinal data, models for clustered longitudinal data, models for data with crossed random factors. Using statistical software to analyse LMMs.

    View more


The information published here is subject to change and may be amended after the publication of this information. The General Regulations (G Regulations) apply to all faculties of the University of Pretoria. It is expected of each student to familiarise himself or herself well with these regulations as well as with the information contained in the General Rules section. Ignorance concerning these regulations and rules will not be accepted as an excuse for any transgression.

Copyright © University of Pretoria 2020. All rights reserved.

FAQ's Email Us Virtual Campus Share