Predictive Analysis

Catalog description

Prerequisites: DDS 8521 with a minimum grade of C. Sufficient knowledge of Statistical Modeling A comprehensive exploration of advanced predictive modeling and machine learning techniques. The course equips students with the skills needed to harness the power of data for making informed decisions. The course dives into regression models, decision trees, support vector machines, and ensemble methods like random forests and gradient boosting. Students will also learn about clustering methods, time series analysis, and the application of these techniques in real-world scenarios. Through hands-on projects and assessments, participants will become proficient in building predictive models, evaluating models, and effectively leveraging machine learning algorithms. It also equips students to interpret and communicate their findings effectively.