Module code | WST 212 |
Qualification | Undergraduate |
Faculty | Faculty of Economic and Management Sciences |
Module content | Introduction to Databases. Database design and use. Data preparation and extraction: basic SQL queries, SQL joins and subqueries. Statistical modelling using database structures. Aims of data analysis (descriptive, inferential and predictive). Stages of conducting a data analysis to solve real-world problems. Sources and types of data and characteristics of extremely large or complex data sets. Introductory machine learning concepts: bias/variance trade-off, model complexity, cross-validation, regularisation, overfitting/underfitting, precision, recall, F1 score, ROC curve and confusion matrix. Data visualisation, data wrangling, supervised learning (linear, local and logistic regression) and unsupervised learning (k-means clustering). Statistical concepts are demonstrated and interpreted through practical coding and simulation within a data science framework. |
Module credits | 12.00 |
NQF Level | 06 |
Programmes |
BCom in Accounting Sciences
BCom specialising in Information Systems BCom specialising in Investment Management BCom specialising in Statistics and Data Science Bachelor of Information Technology in Information Systems [BIT] BSc in Computer Science BSc in Information Technology in Information and Knowledge Systems BSc in Actuarial and Financial Mathematics BSc in Mathematical Statistics BSc in Mathematics 4-year programme BSc in Meteorology BSc in Meteorology 4-year programme BScAgric in Agricultural Economics in Agribusiness Management |
Prerequisites | WST 111, WST 121 or STK 110, STC 122 |
Contact time | 2 lectures per week, 1 practical per week |
Language of tuition | Module is presented in English |
Department | Statistics |
Period of presentation | Semester 1 |
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