Module code | STK 353 |
Qualification | Undergraduate |
Faculty | Faculty of Economic and Management Sciences |
Module content | Data exploration. Data wrangling. Statistical coding. Algorithmic thinking. Sampling: basic techniques in probability, non-probability, and resampling methods. Text mining and analytics. Machine learning: classification and clustering. Statistical concepts are demonstrated and interpreted through practical coding and simulation within a data science framework. |
Module credits | 25.00 |
NQF Level | 07 |
Programmes |
BCom (Informatics) Information Systems
BCom (Statistics and Data Science) BCom (Investment Management) BIT (Information Systems) BSc (Computer Science) BSc (Information and Knowledge Systems) BSc (Meteorology) BSc (Applied Mathematics) BSc (Chemistry) BSc (Mathematical Statistics) BSc (Mathematics) BSc (Physics) |
Service modules | Faculty of Natural and Agricultural Sciences |
Prerequisites | WST 212 |
Contact time | 1 practical per week, 3 lectures per week |
Language of tuition | Module is presented in English |
Department | Statistics |
Period of presentation | Semester 2 |
Copyright © University of Pretoria 2023. All rights reserved.
COVID-19 Corona Virus South African Resource Portal
To contact the University during the COVID-19 lockdown, please send an email to [email protected]
Get Social With Us
Download the UP Mobile App