Course Schedule and Catalog
Course Schedule - Spring Semester 2025
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
MGMT 687 002 (CRN: 27024)
MARKETING ANALYTICS
Long Title: MARKETING ANALYTICS
Department: Management
Instructor: Lee, Jung Youn
Meeting: 6:15PM - 9:30PM M (27-JAN-2025 - 3-MAR-2025)
Part of Term: MBA H
Grade Mode: Standard Letter
Course Type: Lecture
Language of Instruction: Taught in English
Method of Instruction: Face to Face
Credit Hours: 1.5
Course Syllabus:
Course Materials: Rice Campus Store
Restrictions:
Must be enrolled in one of the following Program(s):
MBA Hybrid
MBA for Professional Extended
MBA for Professionals
MBA for Professional Weekend
MBA - Executive Program
MBA
Must be enrolled in one of the following Level(s):
Graduate
Graduate Quadmester
Section Max Enrollment: 45
Section Enrolled: 18
Enrollment data as of: 27-DEC-2024 6:30AM
Final Exam: GR Course-Dept Schedules Exam
Description: Marketing is evolving from an art to a science, with data playing a central role in decision-making. This course gives students the tools to apply analytics to pricing and promotions, focusing on causal inference to understand the true impact of business decisions. A key part of the course is learning to identify which data can—and cannot—answer specific managerial questions, and how careful planning can simplify analysis and boost confidence in the results. This class is very practical and hands-on, working with real-world data from collaborations with leading retailers and consulting firms that bring real challenges into the classroom. Designed for students aiming for careers in consulting, marketing analytics, product management, or strategy development, the course blends theory with coding exercises in R (no prior experience needed). Rather than focusing on advanced statistics or programming, the course prepares students to act as effective bridges between data science teams and managers.