Program Overview

Programme: MCom (Advanced Data Analytics) (Coursework)

Introduction

The MCom (Advanced Data Analytics) programme is offered by the Faculty of Economic and Management Sciences, Department of Statistics.

Programme Information

• Code:
• Faculty: Faculty of Economic and Management Sciences
• Department: Department of Statistics
• Credits: 180
• Duration: Minimum duration of study: 1 year
• NQF level: 09

Admission Requirements

1. Relevant BComHons degree
2. A cumulative weighted average of at least 65% for the honours degree
3. 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 that is supported by the relevant head of department and Postgraduate Studies Committee. Refer to General Academic Regulation G32.

Fundamental Modules

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

Core Modules

• MVA 880: Statistical and machine learning 880
  • Credits: 20.00
  • 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.
• STK 880: Capita selecta: Statistics 880
  • Credits: 20.00
  • Module content: This module covers the most recent literature that discusses current and contemporary research topics in advanced data analytics.
• STK 895: Mini-dissertation: Statistics 895
  • Credits: 100.00
• TRG 880: Data science: analytics and visualisation 880
  • Credits: 20.00
  • 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.
• WST 802: Cyber analytics 802
  • Credits: 20.00
  • 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.

General Academic Regulations

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.