Data Exploration and Visualization for Risk and Decision-Making

 

 Our course work improves the students’ career toolkit to include decision making by visualizing, manipulating, and extracting important concepts from data. The emphasis is for the student to be able to understand the latest techniques in data visualization and knowledge extraction and to leverage such understanding with the latest techniques for decision making and risk analysis.

 

 

EM622 Decision Making Via Data Analysis

 

This course provides a formal introduction to the modern techniques for visualizing data and leverages such techniques with the corresponding problem solving skills necessary to complement data visualization into specific strategic decision making. The student will first learn to use the latest off the shelf software for data visualization.

 

EM623 Data Science and Knowledge Discovery in Engineering Management

 

This course provides an hands-on introduction to the major techniques and solutions to discover knowledge in data and text.  Traditional data mining along with text mining and network analysis will be presented and will be used by the students via open source software, addressing information mining needs on both structured and unstructured data.

 

EM624 Informatics for EngineeringManagement

This course enables the Engineering Management student to acquire the knowledge and skills he/she will need to handle the variety and volume of information encountered in today’s workplace. The course uses Python, which is rapidly becoming the language of choice for information handling and data analysis. Students will work with both structured and semi-structured data.

SYS660 Decision & Risk Analysis

This course is a study of analytic techniques for rational decision-making that addresses uncertainty, conflicting objectives, and risk attitudes. This course covers modeling uncertainty; rational decision-making principles; representing decision problems with value trees, decision trees and influence diagrams; solving value hierarchies; defining and calculating the value of information; incorporating risk attitudes into the analysis; and conducting sensitivity analyses.