KENNESAW STATE UNIVERSITY
GRADUATE COURSE PROPOSAL OR REVISION,
Cover Sheet (10/02/2002)
Course Number/Program Name CS 7050 Data Warehousing and Mining /MS-CS
Department Computer Science / College of Science and Mathematics
Degree Title (if applicable) M.S. Computer Science
Proposed Effective Date Fall, 2012
Check one or more of the following and complete the appropriate sections:
X New Course Proposal
Course Title Change
Course Number Change
Course Credit Change
Course Prerequisite Change
Course Description Change
Sections to be Completed
II, III, IV, V, VII
I, II, III
I, II, III
I, II, III
I, II, III
I, II, III
Notes:
If proposed changes to an existing course are substantial (credit hours, title, and description), a new course with a
new number should be proposed.
A new Course Proposal (Sections II, III, IV, V, VII) is required for each new course proposed as part of a new
program. Current catalog information (Section I) is required for each existing course incorporated into the
program.
Minor changes to a course can use the simplified E-Z Course Change Form.
Submitted by:
Approved
Ying Xie
Faculty Member
10/1/2011
Date
Not Approved
Department Curriculum Committee Date
Approved
Approved
Approved
Approved
Approved
Approved
Not Approved
Department Chair
Date
School Curriculum Committee
Date
School Dean
Date
GPCC Chair
Date
Dean, Graduate College
Date
Not Approved
Not Approved
Not Approved
Not Approved
Not Approved
Vice President for Academic Affairs Date
Approved
Not Approved
President
Date
KENNESAW STATE UNIVERSITY
GRADUATE COURSE/CONCENTRATION/PROGRAM CHANGE
I.
Current Information (Fill in for changes)
Page Number in Current Catalog
Course Prefix and Number
Course Title
Credit Hours
Prerequisites
Description (or Current Degree Requirements)
II.
Proposed Information (Fill in for changes and new courses)
Course Prefix and Number ________CS 7050________________
Course Title __Data Warehousing and Mining
________
Credit Hours 3-0-3
Prerequisites CS 6010 Advanced Algorithms and Data Structures
CS 6050 Advanced Database Systems
Description (or Proposed Degree Requirements)
This course covers prominent algorithms and techniques for developing effective, efficient, and
scalable data warehousing and data mining tools. Topics discussed in this course include: data
visualization, data integration, data warehousing, online analytical processing, data cube technology,
advanced pattern mining, advanced classification analysis, advanced clustering analysis, outlier
detection, data mining trends and research frontiers.
III.
Justification
The world is exploding with digital data. Data Warehousing and Data Mining are the major technologies
to address the “drown in data, but thirsty for knowledge” challenge. This course covers advanced topics
in data warehousing and data mining, focusing on algorithm design and implementation.
Furthermore, this course contributes to the following Program Objectives:
P.L.O. 2: Students will be required to demonstrate that they have in-depth knowledge of at
least two fields within computer science.
P.L.O. 5: Function effectively in teams to accomplish common goals.
P.L.O. 6: Demonstrate the ability to deliver a complete development project, meeting the standards
and requirements.
IV.
Additional Information (for New Courses only)
Instructor: Ying Xie
Text: Data Mining: Concepts and Techniques, Third Edition, Jiawei Han,
Micheline Kamber, and Jian Pei, Morgan Kaufmann, ISBN-10: 0123814790
Prerequisites: CS 6010 Advanced Algorithms and Data Structures & CS 6050
Advanced Database Systems
Objectives:
Upon the completion of the course, students will be able to
 explain and use data visualization and integration techniques
 explain and use data warehousing, online analytical processing, and data cube
technologies
 explain and use advanced data mining techniques
 describe data mining trends and research frontiers
 demonstrate their capability of developing data mining algorithms
 demonstrate their capability of implementing advanced data mining algorithms
Instructional Method
The course will meet primarily for traditional lectures, which are also recorded and
streamed live to remote students.
Method of Evaluation
Evaluation will be through exams, quizzes, grading of lab reports, and attendance at lab sessions.
Evaluation will consist of:
Midterm Exam:
30%
Final Exam:
30%
projects:
20%
Homework assignments:
20%
100%
V.
Resources and Funding Required (New Courses only)
Resource
Amount
Faculty
Other Personnel
Equipment
Supplies
Travel
New Books
New Journals
Other (Specify)
$0
$0
$0
$0
$0
$0
$0
$0
TOTAL
$0
Funding Required Beyond
Normal Departmental Growth
$0
VI. COURSE MASTER FORM
This form will be completed by the requesting department and will be sent to the Office of the
Registrar once the course has been approved by the Office of the President.
The form is required for all new courses.
DISCIPLINE
COURSE NUMBER
COURSE TITLE FOR LABEL
(Note: Limit 16 spaces)
CLASS-LAB-CREDIT HOURS
Approval, Effective Term
Grades Allowed (Regular or S/U)
If course used to satisfy CPC, what areas?
Learning Support Programs courses which are
required as prerequisites
Computer Science
CS 7050
Data WH &Mining
3-0-3
Fall 2012
Regular
APPROVED:
________________________________________________
Vice President for Academic Affairs or Designee __
VII Attach Syllabus
CS 7050 Data Warehousing and Mining
3 Class Hours, 0 Laboratory Hours, 3 Credit Hours
Course Description: This course covers prominent algorithms and techniques for developing
effective, efficient, and scalable data warehousing and data mining tools. Topics discussed in this
course include: data visualization, data integration, data warehousing, online analytical processing,
data cube technology, advanced pattern mining, advanced classification analysis, advanced
clustering analysis, outlier detection, data mining trends and research frontiers.
Prerequisite: CS 6010 Advanced Algorithms and Data Structures
CS 6050 Advanced Database Systems
Instructor: Dr. Ying Xie, CL 3033, yxie2@kennesaw.edu, 678-797-2143
Learning Objectives:
Upon the completion of the course, students will be able to
 explain and use data visualization and integration techniques
 explain and use data warehousing, online analytical processing, and data cube
technologies
 explain and use advanced data mining techniques
 describe data mining trends and research frontiers
 demonstrate their capability of developing data mining algorithms
 demonstrate their capability of implementing advanced data mining algorithms
Textbook: Data Mining: Concepts and Techniques, Third Edition, Jiawei Han, Micheline Kamber, and
Jian Pei, Morgan Kaufmann, ISBN-10: 0123814790.
Instructional Methods and Attendance Policy: The course will meet primarily for traditional
lectures, which are also recorded and streamed live to remote students.
Course Requirements and Assignments: The course will meet for traditional lecture. Students
will be expected to meet for lecture, take all exams and quizzes, participate in group project, and
complete and turn-in project reports as well as homework assignments for grading.
Evaluation and Grading: Evaluation will be through exams, quizzes, grading of lab reports, and
attendance at lab sessions. Evaluation will consist of:
Midterm Exam:
30%
Final Exam:
30%
projects:
20%
Homework assignments:
20%
100%
Academic Honesty: Every KSU student is responsible for upholding the provisions of the Student
Code of Conduct, as published in the Undergraduate and Graduate Catalogs. Section II of the
Student Code of Conduct addresses the University's policy on academic honesty, including
provisions regarding plagiarism and cheating, unauthorized access to University materials,
misrepresentation/falsification of University records or academic work, malicious removal, retention,
or destruction of library materials, malicious/intentional misuse of computer facilities and/or
services, and misuse of student identification cards. Incidents of alleged academic misconduct will
be handled through the established procedures of the University Judiciary Program, which includes
either an "informal" resolution by a faculty member, resulting in a grade adjustment, or a formal
hearing procedure, which may subject a student to the Code of Conduct's minimum one semester
suspension requirement.
Students are encouraged to study together and to work together on project assignments as per the
instructor’s specifications for each assignment; however, the provisions of the STUDENT
CONDUCT REGULATIONS, II. Academic Honesty, KSU Undergraduate Catalog will be strictly
enforced in this class.
Disability policy. Kennesaw State University provides program accessibility and reasonable
accommodations for persons identified as disabled under Section 504 of the Rehabilitation Act of
1973 or the Americans with Disabilities Act of 1990. A number of services are available to help
disabled students with their academic work. In order to make arrangements for special services,
students must visit the Office of Disabled Student Support Services (770-423-6443) and arrange
an individual assistance plan. In some cases, certification of disability is required. It is the
student’s responsibility to take care of this at the beginning of the semester.
Schedule and Topic Coverage (subject to change):
Week
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Lecture Topic
Intro. to data mining
Data visualization
Measure data similarity
Data integration
Data warehousing
OLAP
Midterm Exam
Data cube technology
Mining frequent patterns
Advanced pattern mining
Classification
Advanced classification
Clustering analysis
Advanced clustering analysis
Project presentation
Final Exam
Reference
Ch 1
Ch 2
Ch 2
Ch 3
Ch 4
Ch 4
Exam
Ch 5
Ch 6
Ch 7
Ch 8
Ch 9
Ch 10
Ch 11
Book 2 Ch 11
As per Semester
Schedule