Course Schedule and Catalog
Course Schedule - Fall 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 595 004 (CRN: 15157)
DATA ANALYSIS
Long Title: DATA ANALYSIS
Department: Management
Instructor: Swartz, Richard
Meetings:
8:30PM - 10:00PM R (4-SEP-2025 - 4-SEP-2025)
8:30AM - 12:00PM F (12-SEP-2025 - 12-SEP-2025)
2:00PM - 5:30PM F (12-SEP-2025 - 12-SEP-2025)
6:30PM - 8:00PM R (25-SEP-2025 - 25-SEP-2025)
8:30AM - 12:00PM F (10-OCT-2025 - 10-OCT-2025)
8:30PM - 10:00PM R (23-OCT-2025 - 23-OCT-2025)
8:30AM - 12:00PM F (7-NOV-2025 - 7-NOV-2025)
2:00PM - 5:30PM F (7-NOV-2025 - 7-NOV-2025)
6:30PM - 8:00PM R (20-NOV-2025 - 20-NOV-2025)
2:00PM - 5:30PM F (5-DEC-2025 - 5-DEC-2025)
6:30PM - 8:00PM R (11-DEC-2025 - 11-DEC-2025)
Part of Term: MBA G
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 Hybrid
Must be enrolled in one of the following Level(s):
Graduate
Graduate Quadmester
Section Max Enrollment: 0 (permission required)
Section Enrolled: 25
Enrollment data as of: 15-DEC-2025 3:33AM
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 have 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.