Multivariate Statistical Methods
( 24 Modules )

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

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Multivariate Statistical Methods ( 24 Modules )

Multivariate Statistical Methods
( 24 Modules )