Advanced Data Analysis using R

Week 1
– Experimental Design and Visualization
– Introduction to Experimental Designs
– Principles of Augmented Block Design
– Steps to Implement ABD in R
– Visualization of Groups in ABD
– Solving Case Study Using ABD

Week 2
– Principal Component Analysis (PCA)
– Theory of PCA
– Steps to Perform PCA
– Calculating Component Scores
– Component Correlations
– Applying PCA on real-world data in

Week 3
– Introduction to Biplots
– The Classic Biplot
– Non-linear Axes in Biplots
– Advanced Biplot Techniques
– Generating and analyzing biplots in R

Week 4
– Cluster Analysis
– Introduction to Clustering
– Hierarchical Clustering
– K-Means Clustering
– Density-Based Clustering
– Model-Based Clustering

Week 5
– Multivariate Analysis of Variance (MANOVA)
– Univariate One-Factor Models
– Multivariate Multi-Factor Models
– Profile Analysis in MANOVA
– Assumptions of MANOVA
– Solving a real-world example using MANOVA in R

Week 6
– Data Mining and Text Analysis
– Introduction to Data Mining
– Techniques in Data Mining
– Text Analysis Basics
– Advanced Text Analysis
– Performing data and text mining using R