This article presents a novel approach to introducing principal component analysis (PCA), using summary tables and descriptive statistics. Given its applicability across a variety of academic disciplines, this topic offers abundant opportunity for class discussion and activities. However, teaching PCA in an introductory class can be challenging due to the potential abstraction of multivariate datasets, and especially when students have a minimal background in statistics or data science. This method aims to help teachers bridge the gap between basic descriptive statistics and the more advanced concepts of PCA; this is done by disregarding mathematical optimization, while emphasizing the use of summary tables and the programming language R. The focus is on implementing this method in an introductory tertiary data science course; however, it may potentially be used in higher level courses, and across a variety of disciplines.