I am working on an article that discusses Principal Component Analysis. Here is a sneak-peak.
Principal components analysis is a valuable tool for revealing hidden structure in a dataset with many features/variables. By using PCA, one may be able to:
Identify which variables are important and shape the dynamics of a system
Reduce the dimensionality of the data
Maximize the variance that lies hidden in a dataset and rank them
Filter noise from data
Compress the data
Preprocess data for further analysis or model building.