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COMP 3948 Predictive Modelling
This code intensive course introduces modeling techniques for predicting binary, probability, ordinal and categorical outcomes. Modeling includes popular forms of regression and clustering. Introductory math and statistics behind the fundamental models are discussed and practiced. Use cases and exercises examine eliminating bias at each step of the modeling process. Common sampling methods for training and testing are used to assist with model validation. Techniques for treating missing values, transforming outliers, manufacturing variables and selecting variables are covered. Dimension reduction through principal component analysis is introduced. Analysis of variance is studied and also enhanced with factor analysis. Course work iterates over exploratory analysis and model reporting phases with statistical summaries and visual analytics for reinforcement of learning.
Prerequisite(s): Completion of first year CST and admission into the Predictive Analytics Option.