Factor Analysis
Factor analysis is a statistical method used to identify underlying relationships between observed variables by grouping them into a smaller number of unobserved factors. It helps reduce data dimensionality and uncover latent structures, commonly applied in fields like psychology, social sciences, and market research. The technique involves analyzing correlations among variables to extract factors that explain the shared variance.
Developers should learn factor analysis when working on data-intensive projects involving feature reduction, pattern recognition, or exploratory data analysis, such as in machine learning preprocessing or survey data interpretation. It's particularly useful for simplifying complex datasets, improving model performance by reducing multicollinearity, and gaining insights into hidden constructs in user behavior or system metrics.