One of the most important part of data science is dimension. There are several dimensions in data. The dimensions are represented as n.
For example, suppose that as a data scientist working in a financial company, you have to deal with customer data that involves their credit-score, personal details, salary and hundreds of other parameters.
In order to understand the significant labels that contribute towards our model, we use dimensionality reduction. PCA is a type of reduction algorithm.
With the help of PCA, we can reduce the number of dimensions while keeping all the important ones in our model. There are PCAs based on the number of dimension and each one is perpendicular to the other (or orthogonal). The dot product of all the orthogonal PCs is 0.