Many research papers apply PCA (Principal Component Analysis) to their data and present results to readers without further explanation of the method. When people search on the internet for a definition of PCA, they sometimes get confused, often by terms like "covariance matrix", "eigenvectors" or "eigenvalues". It is not surprising because most explanatory articles focus on detailed calculation process instead of the basic idea of PCA. They are mathematically correct, yet often not intuitively readable at first glance.
For a mathematical method, I believe most people only need to understand the logic and limitations of it and let software packages to do the rest (implementation, calculation, etc.). Here I am trying to explain that PCA is not an impenetrable soup of acronyms but a quite intuitive approach. It can make large-scale data "smaller" and easier to handle.