Dimensionality refers to the number of independent directions or axes in a space. In mathematics and physics, it helps describe the structure of objects. For example, a point has zero dimensions, a line has one dimension, a plane has two dimensions, and a cube has three dimensions. Higher dimensions, such as four or more, are often used in advanced fields like string theory and data science. In data analysis, dimensionality can indicate the number of features or variables in a dataset. High dimensionality can complicate analysis and visualization, leading to challenges like the curse of dimensionality. Reducing dimensionality, through techniques like Principal Component Analysis (PCA), helps simplify data while retaining essential information.