This paper covers the basics of predictive data analytics, statistical computing and visualisation. Students develop an understanding of data science, from the basic skills of data processing and visualisation to building sophisticated descriptive and predictive models.This paper focuses on developing models for classification and prediction. The aim is to use a set of predictor variables to model the outcome of a target variable using techniques such as least squares and logistic regression, k-nearest neighbours, classification and regression trees (CART), and hierarchical classifiers. Shrinkage-based methods for model selection and cross-validation for model evaluation are also introduced.
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