Course Schedule and Catalog
Course Schedule - Fall Semester 2018
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 895 001 (CRN: 14385)
BUSINESS ANALYTICS
Long Title: BUSINESS ANALYTICS
Department: Management
Instructor: Kamakura, Wagner
Meetings:
8:00AM - 12:30PM S (25-AUG-2018 - 25-AUG-2018)
1:30PM - 6:00PM S (8-SEP-2018 - 8-SEP-2018)
8:00AM - 10:00AM F (21-SEP-2018 - 21-SEP-2018)
8:00AM - 12:30PM S (22-SEP-2018 - 22-SEP-2018)
1:30PM - 6:00PM S (6-OCT-2018 - 6-OCT-2018)
1:30PM - 6:00PM F (19-OCT-2018 - 19-OCT-2018)
8:00AM - 12:30PM F (2-NOV-2018 - 2-NOV-2018)
10:30AM - 12:30PM S (3-NOV-2018 - 3-NOV-2018)
1:30PM - 6:00PM S (17-NOV-2018 - 17-NOV-2018)
1:30PM - 6:00PM F (30-NOV-2018 - 30-NOV-2018)
Part of Term: EMBA Term I
Grade Mode: Standard Letter
Course Type: Lecture
Language of Instruction: Taught in English
Method of Instruction: Face to Face
Credit Hours: 3
Course Syllabus:
Course Materials: Rice Campus Store
Restrictions:
Must be enrolled in one of the following Program(s):
MBA - Executive Program
Must be enrolled in one of the following Level(s):
Graduate
Section Max Enrollment: 65
Section Enrolled: 62
Enrollment data as of: 30-JUN-2025 8:34PM
Final Exam: GR Course-Dept Schedules Exam
Description: The ever-increasing capacity of computers to analyze data, and the explosion of the amount of data available, has resulted in an increased role for data analysis as an aid to business decision-making. This course exposes the student to the most important ideas and methods relevant for data analysis in a business context. Emphasizing practical applications to real problems, the course covers the following topics: Sampling, Descriptive Statistics, Probability Distributions, and Regression Analysis. Students are strongly encouraged to bring data from work; projects from previous years have returned significant monetary value to students’ current employers and examples of these projects will be provided in class. Repeatable for Credit.