• Introduction to analytics: a general introduction into analytics and some of the techniques that are in common use.
• Simple regression problems: simple and multiple linear regression and model diagnostics.
• Model selection and assessment: Cross validation and bootstrapping. Penalised regression and shrinkage.
• Classification: KNN, clustering, logistic regression, Linear Discriminant analysis and associated diagnostics.
• Advanced regression techniques: polynomial regression, splines, local regression, GAMs, trees and random forests.
• Workflow development: Throughout we will develop a workflow of training, testing and assessing models that can be extended to techniques not directly covered.

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