Applied Statistics: Inference and Modeling
This course teaches students how to build on statistical foundations for data science with attention to the analysis of multivariate data. Basic machine learning methods – such as linear discriminant analysis, logistic regression and principal components analysis – are discussed. Emphasis is on interpretation of the analysis rather than calculations.
Multiple Linear Regression and Variable Selection, Multivariate Analysis of Variance (MANOVA), Linear and Quadratic Discriminant Analysis, Unsupervised Learning (Clustering), Methods for Categorical Variables (Explanatory and Response), Autoregressive Models for Time Series Data, Basic Bootstrap
- SAS, R