Programme: MSc (Advanced Data Analytics) (Coursework)

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Code Faculty Department
02250195 Faculty of Natural and Agricultural Sciences Department: Statistics
Credits Duration NQF level
Minimum duration of study: 1 year Total credits: 180 NQF level:  09

Programme information

Details of compilation of curriculum are available from the Head of the Department of Statistics as well as from the departmental postgraduate brochure.

A candidate must compile his/her curriculum in consultation with the head of department or his representative. Refer to the Departmental website for further information.

Admission requirements

  1. BScHons in Mathematical Statistics degree or relevant honours degree
  2. A weighted average of at least 65% at honours level
  3. At least 65% for the research component at honours level, but students with a weighted average of at least 70% or more will receive preference
  4. An admission examination may be required

Note: Additional modules may be required in order to reach the desired level of competency

Promotion to next study year

The progress of all master's candidates is monitored biannually by the supervisor and the postgraduate coordinator. A candidate's study may be terminated if the progress is unsatisfactory or if the candidate is unable to finish his/her studies during the prescribed period.

Subject to exceptions approved by the Dean, on recommendation of the relevant head of department, and where applicable, a student may not enter for the master's examination in the same module more than twice.

Minimum credits: 180

All master’s students in Statistics/Mathematical Statistics should enrol for STK 899 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.

Students should choose any four (4) of the elective modules from the list, to the maximum value of 80 credits.

Fundamental modules

  • 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.

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Core modules

Elective modules

  • Module content:

    Unsupervised learning: deterministic clustering, model-based clustering, latent class and behavioural analytics, dimension reduction. Natural language processing and topic modelling; recommender systems. Organisation of data, data wrangling and data structure exploration.

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  • Module content:

    This module covers the most recent literature that discusses current and contemporary research topics in advanced data analytics.

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  • Module content:

    Difference equations. Lag operators. Stationary ARMA processes. Maximum likelihood estimation. Spectral analysis.  Vector processes. Non-stationary time series. Long-memory processes.

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  • Module content:

    Supervised learning and applications. Multicollinearity, ridge regression, the LASSO and the elastic net. Parametric and nonparametric logistic regression and nonlinear regression. Survival regression. Regression extensions: Random forests MARS and Conjoint analysis. Neural networks.

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  • Module content:

    Reviewing, from a statistical perspective, the cyber-infrastructure ecosystem including distributed computing, multi node and distributed file eco systems, such as Amazon Web Services. Structured and unstructured data sources, including social media data and image data. Setting up of large data structures for analysis. Algorithms and techniques for computing statistics and statistical models on distributed data. Software to be used include, Hadoop, Map reduce, SAS, SAS Data loader for Hadoop.

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The regulations and rules for the degrees published here are subject to change and may be amended after the publication of this information.

The General Academic Regulations (G Regulations) and General Student Rules apply to all faculties and registered students of the University, as well as all prospective students who have accepted an offer of a place at the University of Pretoria. On registering for a programme, the student bears the responsibility of ensuring that they familiarise themselves with the General Academic Regulations applicable to their registration, as well as the relevant faculty-specific and programme-specific regulations and information as stipulated in the relevant yearbook. Ignorance concerning these regulations will not be accepted as an excuse for any transgression, or basis for an exception to any of the aforementioned regulations.

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