Introduction to mathematical modeling in data science. Classification ofmathematical models into linear, nonlinear, and regularized models. Exploration of models for supervised learning (regression, classification) and unsupervised learning (dimension reduction, clustering). Tree-based models and ensemble techniques such as random forests and boosting. Case studies on ridge regression, lasso regression, and support vector machines, with practical applications and insights into model selection and evaluation.