COB 17-08 REV COURSE BIZA 42000
Purdue Northwest Curriculum Document
Program Name:
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- Document No: COB 17-08 REV COURSE BIZA 42000
- Proposed Effective Date: Fall 2018
- Submitting Department: Quantitative Business Studies/College of Business
- Date Reviewed by Department: 1/26/2018
- Submission Date: 1/29/2018
- Date Reviewed College/School Curriculum Committee: 1/31/2018
- Contact Person(s): Serdar Turedi, Assistant Professor of Business Analytics and Raida Abuizam, Department Head of Quantitative Business Studies
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- Approval by Faculty Senate:
- Date Reviewed by Senate Curriculum Committee:
- Name(s) of Library Staff Consulted: Not Applicable
- Will New Library Resources Used?: No
- Form 40 Needed?: Yes
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Task: Course Change or New Course Approval
- Program Name:
- Degree Name(s):
Section I: This section is for changes in programs, minors and certificates
List the major changes in each program of study, minor or certificate.
Impact on Students:
Not Applicable
Impact on University Resources:
Not Applicable
Impact on other Academic Units:
Not Applicable
Section II: This section is for changes in courses only
Subject:
Add BIZA 32500 – Applied Business Statistics as a prerequisite and remove ISM 32000 and MGMT 32000 from the prerequisite list.
Justification:
ISM 32000 is not a required course for business analytics students as a result of the change in the plan of study.
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Current:
BIZA 42000 – DECISION ANALYTICS:
Credits: 3 (Class 3, Lab 0)
Prerequisites: ISM 32000 or MGMT 32000
Co-Requisites: N/A
An overview of spreadsheet models available to analyze decision problems in various functional fields including finance, marketing, and operations. The course topics will include optimization, simulation and decision making under uncertainly. Typically offered Fall Spring Summer.
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Proposed:
BIZA 42000 – DECISION ANALYTICS:
Credits: 3 (Class 3, Lab 0)
Prerequisites: BIZA 32500
Co-Requisites: N/A
An overview of spreadsheet models available to analyze decision problems in various functional fields including finance, marketing, and operations. The course topics will include optimization, simulation and decision making under uncertainty.
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Course Objectives / Learning Outcomes:
Impact on Students:
Not Applicable.
Impact on University Resources:
Not Applicable. Course will be taught with existing faculty
Impact on other Academic Units:
Not Applicable.