| Code | Faculty |
|---|---|
| 07250066 | Faculty of Economic and Management Sciences |
| Credits | Duration | NQF level |
|---|---|---|
| Minimum duration of study: 2 years | Total credits: 180 | NQF level: 09 |
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.(See Regulations G.32 and G.36.)
Minimum credits: 180
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:
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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