Graduate Curriculum Committee Course Proposal Form for Courses Numbered 5000 and Higher

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Graduate Curriculum Committee Course Proposal Form

for
Courses Numbered 5000 and Higher


Note: Before completing this form, please carefully read the accompanying instructions.


1. Course prefix and number:


2. D
ate:


3. Requested action
(check only
one

box)
:

x

New Course


Revision of Active Course


Revision & Unbanking of a Banked Course


Renumbering of an Existing Course from


from

#

to

#


4. Justification (assessment or accreditat
ion based) for new course or course
revision or course renumbering:

Data mining is an important and growing area of computer science that is not addressed by current courses in computer
science.
Faculty have determined that t
his course is needed to respon
d to advances in the discipline of computing.


5. Course description exactly as it should appear in the next catalog:

6840
.
Data Mining

(
3
)
Topics on

data mining concepts and techniques

and state of the art in data mining
,
including
association rule mini
ng
,
classification
,
clustering,
data mining on complex type of data,
and other data mining algorithms
and applications.



6. If this is a course revision, briefly describe the requested change:


7. Graduate catalog page number from curren
t graduate catalog:




8. Course credit:

Lecture Hours

3

Weekly

OR


Per Term

Credit Hours

3

s.h.

Lab


Weekly

OR


Per Term

Credit Hours


s.h.

Studio


Weekly

OR


Per Term

Credit Hours


s.h.

Practicum


Weekly

OR


Per Term

Credit Hours


s.h.

Internship


We
ekly

OR


Per Term

Credit Hours


s.h.

Other (e.g., independent study) Please explain.



Total Credit Hours

3

s.h.


9. Anticipated annual student enrollment:


10. Affected degrees or academic programs:

Degree(s)/Course(s)

Current

Catalog Page

Chang
es in Degree Hours

M
S

in
Computer Science

29
5
-
29
7



11. Overlapping or duplication with affected units or programs:

x

Not Applicable


Notification & response from affected units is attached

1
0

CSCI

6
840

1/
9
/0
9


p.
29
6

12. Approval by the Council for Teacher Education (required fo
r courses affecting
teacher education programs):

x

Not Applicable


Applicable and CTE has given their approval.

13.

Statements of support:

a. Staff

x

Current staff is adequate


Additional Staff is needed (describe needs in the box below):


b. Facil
ities

x

Current facilities are adequate


Additional Facilities are needed (describe needs in the box below):



c. Library

x

Initial library resources are adequate


Initial resources are needed (in the box below, give a brief
explanation and an estimat
e for the cost of acquisition of required
initial resources):


d. Computer resources

x

Unit computer resources are adequate


Additional unit computer resources are needed (in the box below,
give a brief explanation and an estimate for the cost of acqui
sition):


x

ITCS Resources are not needed


The following ITCS resources are needed (put a check beside each
need):


Mainframe computer system


Statistical services


Network connections


Computer lab for students


Software

Approval from the
Director of ITCS attached


14. Course information:
see Instructions for Completing the Graduate Curriculum
Committee Course Proposal Form
for more detail.

a.

Textbook(s): author(s), name, publication date, publisher, and city/state/ country


Required
textbo
oks

Data Mining: Concepts and Techniques
(2nd Ed.), by J. Han and M.
Kamber, Morgan Kaufmann Publishers, 2006. ISBN: 1
-
55860
-
901
-
6

Optional reading

Assigned papers


b.

Course objectives student


centered behavioral objectives for the course
:


The objectiv
es of this course is for students to learn
data mining concepts and techniques and
state of the art in data mining

and how to apply data mining methods on various types of data.

Upon completion of this course each student will be able to:



Explain

the fund
amental concepts of data mining



Master various data mining algorithms



Apply data mining methods for different types of data to discover patterns



I
mplement data mining algorithms


c.

A course topic outline

1.

Introduction to data mining

2.

Data preprocessing

3.

Assoc
iation rule mining

4.

Classification

5.

Clustering

6.

Data warehousing

7.

Data mining on
complex type of data (
time
-
series
data, spatial data, t
ext data, web data,
biology
data)

8.

Data mining applications and trends


d.

A list of course assignments and weighting of eac
h assignment and the
grading/evaluation system for determining a grade.


Assignments and Grading:

Assignments




20%

Term Project




3
0%

Midterm
Exam




2
0%


Final
Exam




3
0%



Grade Scale:

90
-
100 points




A

80
-
89 points




B

70
-
79 points




C

Below 70




F