Multivariate statistics covers a variety of topics falling mainly in two areas: descriptive (clustering, principal components, correspondence analysis), and predictive (models with predictor variables and an outcome variable). Various multivariate techniques constitute the core of what is used in research (especially models that explain relationships), and in data mining (prediction, classification, segmentation).
Here are the core courses in multivariate statistics at the Institute. There are many others, especially in data mining, not listed here.
Logistic Regression Multiple predictor variables, binary outcome
Multivariate Statistics General overview of multivariate data, plus focus on correspondence analysis and discriminant analysis
Generalized Linear Models General conceptual framework for linear models
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