This course will expose students to the main kinds of analytical models that can help improve their capacity in making decisions based on data. Such models are of two major kinds: predictive models (which use data to predict what is otherwise unknown), and prescriptive models (which use data, including the predictions made using data, to advise on the best course of action). This course will give an introductory understanding of both kinds of models, including models for predicting quantities (such as regressions), models for predicting events (such as classification and regression tree, random forests, and the likes), decision tree and Monte Carlo simulation models, and optimization models (linear programming). The focus will be made not on the technical issues, but rather on the conceptual understanding and the ability to implement these models using the examples of software and case studies provided during the course.
This course will expose students to the main kinds of analytical models that can help improve their capacity in making decisions based on data. Such models are of two major kinds: predictive models (which use data to predict what is otherwise unknown), and prescriptive models (which use data, including the predictions made using data, to advise on the best course of action). This course will give an introductory understanding of both kinds of models, including models for predicting quantities (such as regressions), models for predicting events (such as classification and regression tree, random forests, and the likes), decision tree and Monte Carlo simulation models, and optimization models (linear programming). The focus will be made not on the technical issues, but rather on the conceptual understanding and the ability to implement these models using the examples of software and case studies provided during the course.
