Code | Faculty | Department |
---|---|---|
07250069 | Faculty of Economic and Management Sciences | Department: Economic and Management Sciences Dean's Office |
Credits | Duration | NQF level |
---|---|---|
Minimum duration of study: 1 year | Total credits: 180 | NQF level: 09 |
General Admission Regulations:
Refer to the UP Institutional Admissions Policy as well as the General Academic Regulations and Student Rules (Go to www.up.ac.za./yearbooks/home and click on General Rules and Regulations, then on General Academic Regulations and Student Rules)
Minimum admissions requirements
Note:
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. Refer to General Academic Regulation G32.
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:
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.
Module content:
This module covers the most recent literature that discusses current and contemporary research topics in advanced data analytics.
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:
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.
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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