Program Overview

Programme: MCom specialising in Advanced Data Analytics (Coursework)

Programme Information

The MCom specialising in Advanced Data Analytics (Coursework) is a postgraduate programme offered by the Faculty of Economic and Management Sciences at the University of Pretoria. The programme is designed to equip students with advanced knowledge and skills in data analytics, with a focus on statistical and machine learning techniques.

Admission Requirements

General Admission Regulations

• All applications must be accompanied by certified full academic transcripts from undergraduate to current level.
• Certified copy of ID or passport.
• A research concept note (not applicable to honours or coursework master's degrees): A description of the proposed research field indicating a research topic and the broad scope of the proposed study, not exceeding 500 words.
• All applicants with international qualifications must submit a SAQA evaluation of the completed qualification or a comprehensive Foreign Qualification Report.
• TOEFL or IELTS or Pearson Test of English or Oxford Test of English test results (if applicable).
• Certified copy of passport.

Minimum Admissions Requirements

• Bachelor of Commerce Honours specialising in Mathematical Statistics or a relevant honours degree.
• A weighted average of at least 65% at honours level, but students with a weighted average of at least 70% will receive preference.
• An average of at least 65% for the research component at honours level.

Other Programme-Specific Information

• 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 relevant head of department) that is supported by the relevant head of department and Postgraduate Studies Committee.

Fundamental Modules

• STK 899: Research orientation 899 (Credits: 0.00)
  • 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.

Core Modules

• STK 895: Mini-dissertation: Statistics 895 (Credits: 100.00)

Elective Modules

• MVA 880: Statistical and machine learning 880 (Credits: 20.00)
  • 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.
• STK 880: Capita selecta: Statistics 880 (Credits: 20.00)
  • This module covers the most recent literature that discusses current and contemporary research topics in advanced data analytics.
• TRA 880: Analysis of time series 880 (Credits: 20.00)
  • Difference equations.
  • Lag operators.
  • Stationary ARMA processes.
  • Maximum likelihood estimation.
  • Spectral analysis.
  • Vector processes.
  • Non-stationary time series.
  • Long-memory processes.
• TRG 880: Data science: analytics and visualisation 880 (Credits: 20.00)
  • 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.
• WST 802: Cyber analytics 802 (Credits: 20.00)
  • 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.