Module #1 Introduction to Multivariate Analysis Overview of multivariate methods, importance, and applications
Module #2 Types of Multivariate Data Classification of multivariate data, examples, and data preparation techniques
Module #3 Descriptive Statistics for Multivariate Data Measures of central tendency, variability, and correlation for multivariate data
Module #4 Visualization of Multivariate Data Graphical methods for visualizing and exploring multivariate data
Module #5 Principal Component Analysis (PCA) Introduction to PCA, applications, and interpretation of results
Module #6 PCA:Mathematical Formulation and Implementation Mathematical formulation of PCA, eigenvalues, eigenvectors, and implementation in R/Python
Module #7 Independent Component Analysis (ICA) Introduction to ICA, differences from PCA, and applications
Module #8 ICA:Mathematical Formulation and Implementation Mathematical formulation of ICA, fastICA algorithm, and implementation in R/Python
Module #9 Factor Analysis Introduction to factor analysis, types of factor analysis, and applications
Module #10 Factor Analysis:Mathematical Formulation and Implementation Mathematical formulation of factor analysis, estimation methods, and implementation in R/Python
Module #11 Cluster Analysis Introduction to cluster analysis, types of clustering, and applications
Module #12 Hierarchical Clustering Agglomerative and divisive clustering, dendrograms, and implementation in R/Python
Module #13 K-Means Clustering K-means algorithm, implementation in R/Python, and applications
Module #14 Discriminant Analysis Introduction to discriminant analysis, types of discriminant analysis, and applications
Module #15 Linear Discriminant Analysis (LDA) Mathematical formulation of LDA, Fishers discriminant, and implementation in R/Python
Module #16 Quadratic Discriminant Analysis (QDA) Mathematical formulation of QDA, implementation in R/Python, and applications
Module #17 Multivariate Regression Analysis Introduction to multivariate regression, assumptions, and applications
Module #18 Multivariate Regression:Mathematical Formulation and Implementation Mathematical formulation of multivariate regression, implementation in R/Python, and model evaluation
Module #19 Canonical Correlation Analysis (CCA) Introduction to CCA, mathematical formulation, and implementation in R/Python
Module #20 Partial Least Squares (PLS) Regression Introduction to PLS, mathematical formulation, and implementation in R/Python
Module #21 Multivariate Time Series Analysis Introduction to multivariate time series, stationarity, and seasonal decomposition
Module #22 Vector Autoregression (VAR) Models Introduction to VAR models, mathematical formulation, and implementation in R/Python
Module #23 Model Selection and Validation Model selection criteria, cross-validation, and hyperparameter tuning for multivariate models
Module #24 Course Wrap-Up & Conclusion Planning next steps in Multivariate Statistical Methods career
