Ilbin Lee, PhD
Fall Term 2025 (1930)
OM 420 - Predictive Business Analytics
3 units (fi 6)(EITHER, 3-0-0)
Application of predictive statistical models in areas such as insurance risk management, credit risk evaluation, targeted advertising, appointment scheduling, hotel and airline overbooking, and fraud detection. Students will learn how to extract data from relational databases, prepare the data for analysis, and build basic predictive models using data mining software. Emphasizes the practical use of analytical tools to improve decisions rather than algorithm details. Prerequisite: MGTSC 352 or OM 352.
LECTURE A01 (53160)
2025-09-02 - 2025-12-08
MW 11:00 - 12:20
OM 620 - Predictive Business Analytics
3 units (fi 6)(EITHER, 3-0-0)
Application of predictive statistical models in areas such as insurance risk management, credit risk evaluation, targeted advertising, appointment scheduling, hotel and airline overbooking, and fraud detection. Students will learn how to extract data from relational databases, prepare the data for analysis, and build basic predictive models using data mining software. Emphasizes the practical use of analytical tools to improve decisions rather than algorithm details. Prerequisite: MGTSC 501.
LECTURE A01 (53162)
2025-09-02 - 2025-12-08
MW 11:00 - 12:20
Winter Term 2026 (1940)
OM 420 - Predictive Business Analytics
3 units (fi 6)(EITHER, 3-0-0)
Application of predictive statistical models in areas such as insurance risk management, credit risk evaluation, targeted advertising, appointment scheduling, hotel and airline overbooking, and fraud detection. Students will learn how to extract data from relational databases, prepare the data for analysis, and build basic predictive models using data mining software. Emphasizes the practical use of analytical tools to improve decisions rather than algorithm details. Prerequisite: MGTSC 352 or OM 352.
LECTURE B01 (81506)
2026-01-05 - 2026-04-10
MW 11:00 - 12:20
LECTURE B02 (86409)
2026-01-05 - 2026-04-10
TR 12:30 - 13:50
OM 620 - Predictive Business Analytics
3 units (fi 6)(EITHER, 3-0-0)
Application of predictive statistical models in areas such as insurance risk management, credit risk evaluation, targeted advertising, appointment scheduling, hotel and airline overbooking, and fraud detection. Students will learn how to extract data from relational databases, prepare the data for analysis, and build basic predictive models using data mining software. Emphasizes the practical use of analytical tools to improve decisions rather than algorithm details. Prerequisite: MGTSC 501.
LECTURE B01 (81502)
2026-01-05 - 2026-04-10
MW 11:00 - 12:20