Purdue University Northwest > Faculty Senate > News > Senate Documents > COB 17-07 NEW COURSE BIZA 41000

COB 17-07 NEW COURSE BIZA 41000

January 29, 2018

Purdue Northwest Curriculum Document

Program Name:

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  • Document No: COB 17-07 NEW COURSE BIZA 41000
  • 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:

Introduction of new course – BIZA 41000 – Data Mining in Business

Justification:

New course to enhance business analytics students’ understanding of data mining

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Current:

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Proposed:

BIZA 41000 – DATA MINING IN BUSINESS:

Credits: 3 (Class 3, Lab 0)

Prerequisites: BIZA 35000

Co-Requisites: N/A

The objective of this course is to provide an introduction to data mining tools. This course will introduce data mining tools, such as neural networks, decision trees, discriminant analysis and association analysis to extract the hidden information in the data.

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Course Objectives / Learning Outcomes:

  1. Students will comprehend different data properties.
  2. Students will understand data mining algorithms and methods.
  3. Students will apply different data mining techniques and solve problems.

Impact on Students:

Students will learn how to extract information from data. Extraction of information will help them discover the hidden patterns.

Impact on University Resources:

Not Applicable. Course will be taught with existing faculty

Impact on other Academic Units:

Not Applicable.