Data Science and Machine Learning

This course provides an overview of modern data science and machine learning (DSML) techniques, contrasting them with a traditional statistical approach. Students will learn how analysts can transition from classical statistics to more advanced predictive modeling and algorithmic data analysis. The course will cover both the theoretical and applied aspects of powerful DSML tools, such as neural networks, support vector machines, decision trees, random forest, gradient boosting, XGBoosting, model selection, model averaging, cluster analysis, and text mining. Upon completing this course, students will leverage modern modeling techniques to extract insights, predict outcomes, and optimize decisions.

GR - Graduate

N - Course May Not Be Repeated