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The science of data analytics 353


 
Modulekode STK 353
Kwalifikasie Undergraduate
Fakulteit Faculty of Economic and Management Sciences
Module-inhoud

Introduction to coding: data types, basic arithmetic, logical comparisons, functions, loops, conditional statements, packages. Data exploration and visualisation. Visualisation best practices. Data wrangling: data cleaning, missing values, duplicate data, outliers. Data transformation. Principal component analysis. Statistical coding. Algorithmic thinking.  Sampling: basic techniques in probability, non-probability, and resampling methods, Monte Carlo, probability integral transformation, bootstrap method, acceptance/rejection algorithm. Machine learning: train/test split, performance metrics, classification and clustering, performance metrics, cross-validation. Supervised and unsupervised learning: linear regression, decision tree, random forest, naïve Bayes, K-nearest neighbour, hierarchical clustering. Interpretation and communication of results. Text mining and analytics: topic modelling and word embeddings. Statistical concepts are demonstrated and interpreted through practical coding and simulation within a data science framework.
Modulekrediete 18.00
NQF Level 07
Service modules Faculty of Natural and Agricultural Sciences
Prerequisites WST 212
Contact time 1 practical per week, 2 lectures per week
Language of tuition Module is presented in English
Department Statistics
Period of presentation Semester 2
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