For a large variety of discrete choice models (or contingency table models) efficientand stable maximum likelihood methods can be constructed basedon the majorization method. The course introduces majorization methods for algorithm construction. We show how to use the majorization principle to reduce complicated optimization problems to sequences of weighted or unweighted least squares problems. Majorization methods are then applied to data analysis techniques used in economics, political science, psychometrics, ecology, sociology, and education.