Dimension reduction for data science
The discipline of data science requires transparency to strive. Problem-solving in data science often employs multiple methods packaged in one or more bundles by algorithm inventors or software developers. Due to the complexity of the data in terms of volume, dimension, structure  and mode,  new methods tend to contain existing methods as the inner layers.  This causes the danger of gradual loss in transparency to both users and future developers. 
 
Dimension reduction (DR) is a key component in  many data-analytic algorithms or software packages for application in data science.  Principal component analysis(PCA), being implemented  as an inner layer in  numerous packages, has become a household name in lieu of  dimension reduction across a variety of scientific disciplines.  In addition to PCA, many methods for dimension reduction have been developed. The goal of this course is to present a general statistical framework of dimension reduction and discuss the shared and unique properties of different DR methods. 

This is a graduate course for students who have  an adequate undergraduate-level of  statistics, applied mathematics, data science, data engineering, or related training.  Exceptionally strong undergraduate students are also welcome to take this course. 

The class will meet once a week for one and a half hour