College of Arts and Sciences
Chairperson's Application for Approval of a New Course
TO: CAS Academic Dean
FROM: Chandra N Sekharan
DEPARTMENT: Computer Science
1. PROPOSED NEW COURSE:
• Course Number: COMP 300
• Credit Hours: 3
• Course Title: Data Warehousing and Data Mining
• Title Abbreviation: Data Warehouse & Mining
(Titles longer than 25 character positions must be abbreviated to not more than 25 character positions, exclusive
of cross listing notations, for computer printouts. Count spaces and punctuation marks into total. Please limit
punctuation to colons, ampersands (&), and dashes, if possible.)
(NOTE: All cross-listings must be approved by the chairperson(s) of the cross-listed department(s).)
• Course Number: N/A
• Credit Hours: N/A
• Course Title: N/A
• Title Abbreviation:
Signature(s) of Concurring Chairperson (on original forms): Date
3. PLEASE ANSWER THE FOLLOWING REGARDING THIS PROPOSED NEW COURSE:
• What, if any, will be the prerequisites for this course? COMP 251
• Will it be a prerequisite for any other course? No.
• Will it be required for the major? Yes, to the newly proposed BA major
• Should any course presently offered be dropped? No.
• Date or term in which this new course becomes effective: Fall 2004
• Which full-time faculty members will be prepared to teach or supervise this course? The course can be
taught by any computer science faculty, but most notably by Chandra Sekharan, and George Thiruvathukal.
• Are available material resources (e.g., library, laboratory) adequate for the course? Yes.
• Are adequate resources available in the library? (Yes or No) Yes.
• If no, approximate cost of obtaining sufficient resources:
Signature of Bibliographer (on original form): Date
• Explain briefly the writing component of this course.: Homeworks and Programming Assignments.
• Has this course been offered as a special topics course? No. However, we have taught data warehousing
and data mining techniques within some of our existing database classes such as COMP 251 and COMP
• If yes, how many times? N/A
• When? N/A
• What enrollment? N/A
4. REASONS FOR ADDING THIS COURSE: The course is fundamental to one of the tracks in the B.A.
Computer Technology major, viz., Knowledge Databases.
5. CATALOG DESCRIPTION OF NEW COURSE: COMP 300: 3 credits: This course deals with techniques
of storing of volumes of data, and building data warehouse schemas. Techniques for information retrieval such as
OLAP slicing, dicing, roll-up are examined. Data mining techniques such as classification, association rules, etc.
are covered using standard software packages.
6. PLEASE INCLUDE A SYLLABUS (and bibliography, if available). Attached.
7. SIGNATURES: (on original form)
• Chairperson Date
• Academic Council Representative Date
• Academic Dean Date
• Registrar's Approval of Course Number Date
After approval has been given, and the course added to the Title Database, this form will be returned to the
Academic Dean who will forward it to the chairperson of the initiating department.
COMP 300 Data Warehousing and Data Mining
Data warehousing and data mining are two major areas of exploration for knowledge discovery
in databases. These topics have gained great relevance especially in the 1990’s with the web
data growing at an exponential rate. As more data is collected by businesses and scientific
institutions alike, knowledge exploration techniques are needed to gain useful business
intelligence. This course is conceived to cover a wide spectrum of industry standard techniques
using widely available database and tools software for knowledge discovery. The course teaches
high volume data processing mechanisms by first building warehouse schemas such as
snowflake, and star. Then OLAP query retrieval techniques are introduced. Data mining is for
relatively unstructured data for which more sophisticated techniques are needed. The course
aims to cover powerful data mining techniques including clustering, association rules, and
After taking this course, students should be able to:
• Understand how to store large volumes of data in a database server environment.
• Use data schemas for warehouse environment.
• Use OLAP queries in a data warehouse.
• Know basic techniques for both directed and undirected knowledge discovery.
• Know and use software package techniques for mining.
• Have a good grasp of data mining techniques, such as association rules, clustering etc.
Data Mining: Concepts and Techniques by Jiawei Han and Micheline Kamber, Morgan
Class Participation and Attendance
Student attendance is required. Hence, students are expected to arrive to class punctually.
Students are expected to use software packages and hence attending computer lab sessions is a
Homework Assignments 30 points
Project 10 points
Midterm 20 Points
Quizzes 10 Points
Final Exam 30 Points
90-100 = A
85-89 = B+
80-84 = B
75-79 = C+
70-74 = C
65-69 = D
64 and lower = F
Students should read and understand the College of Arts and Sciences’ policy on academic
integrity, which is described in the Undergraduate Studies Catalogue. Students found in violation
of the policy could fail an assignment or the course and might be subjected to other penalties up
to, and including, expulsion from the university.
Class Schedule and Readings
Week 1: Review of Databases
Week 2: Applications of Data Warehousing and Data
Week 3: Warehouse modeling with Snowflake and
Week 4: OLAP: slicing and dicing, roll-up queries
Week 5: Data Preparation
Week 6: Oracle Data warehousing tools.
Week 7: Data Mining primitives, Architecture
Week 8: Mining Concept description:
Week 9: Mining association rules
Week 10 Classification and Prediction
Week 11 Cluster Analysis
Week 12 Introduction to Weka Software
Week 13 Business Examples for Mining and Weka
Week 14 Mining trends and conclusion.
The class schedule closely follows the order of chapters in the text.