California State University Dominguez Hills
College of Business and Public Policy
Master Course Outline
CIS 540 Data Warehousing and Data Mining
Course Number: CIS 540
Course Title: Data Warehousing and Data Mining
Catalog Description of Course:
This course will present the current state of the art in strategies for enterprise-access to data for decision support
and knowledge discovery. It will cover industrial practice in business intelligence of the following three
1. Data Warehousing – extracting, cleaning, and organizing data from transactional databases
2. Data Mining – taking warehouse data and extracting patterns and relationships
3. Decision Support – taking the patterns extracted from the data and making management decisions
Course Sequencing and Relationship to Mission:
CIS540 is an elective course for the MBA with IT Management concentration program
This course provides a theoretical as well as a practical basis for understanding the enterprise data
management systems for decision support and knowledge discovery
Course Educational Objectives
Upon completion of this course, students should be able to
• Characterize a data warehouse
• Know the design techniques and logical architectures for a data warehouse
• Discuss data cleansing and reduction issues
• Define dimensional modeling as well star and snowflake schemas
• Differentiate temporal and spatial dimensions of data
• Create a data warehouse
• Understand data mining tasks
• Employ parametric and nonparametric models in data analysis
• Perform classification and cluster analysis
How are Course Educational Objectives Measured?
Course objectives are measured through written assignments, research papers, and projects. The
evaluations focus on how well concepts and principles of data warehousing and mining are presented, the validity
of data analysis and conclusions. Written assignments and projects are used to evaluate the knowledge and
understanding of principles and concepts taught. The weight (in terms of class time) given to each component of
the course content is indicated in the Course Content section of this outline
Course Expectations and Policies
• Course Expectations: Each student is expected to read the assigned material and complete the assignments
when they are due. The university standard for course workload is two hours of outside work for every
hour in class, i.e., a 3-unit course requires 9 hours of work/week.
• Participation Policy: It is expected that students will study the course materials regularly to follow the
course schedule and participate in the class discussion throughout the term. If you fall behind, it is your
responsibility to make additional efforts to catch up with the class. Class participation will be used in the
final determination of grades and can alter your grade up or down.
• Academic Integrity: Cheating or plagiarism in connection with an academic program or class at a campus
is subject to discipline as provided in Sections 41301 through 41304 of Title 5, California Code of
Regulations. Please see the University Catalog for further information.
• Disabled Students Services: Students with verified disabilities are eligible for a variety of support
services from the Disabled Services Office. Information regarding special facilities and services available
to students with a disability may be obtained from the Director of Disabled Student Services Office,
located in WH B-250.
• Due Dates/Make Up Work: Examinations must be taken as scheduled. Make-ups will be allowed only if
the student has contacted the professor before the scheduled date, detailing a serious problem. No make-
ups are given for quizzes. Assignments are due as scheduled. Assignments submitted late will be
penalized at 5 percentage points per weekday.
Course grades will be based on a weighted average of the following components:
• Class participation (25%): You are expected to participate actively in the discussion of cases and readings,
and contribute to the learning experience of the class.
• Written Assignments (25%).
• Term Project/Final (50%): The paper should focus on a timely and significant application of data
warehouse/mining. Students should discuss potential topics for the term paper with the instructor.
Important: Keep your graded work until the end of the course; recording errors may occur.
Letter Percentage of total
Grade points available
A 93% - 100%
A- 90% - 92%
B+ 87% - 89%
B 83% - 86%
B- 80% - 82%
C+ 77% - 79%
C 73% - 76%
C- 70% - 72%
D+ 67% - 69%
D 60% - 66%
F Below 60%
Course Content Hours
1. Introduction 3
Basic Elements of the Data Warehouse
2. Principles of Data Warehousing 9
The Business Dimensional Lifecycle
Project Planning and Management
Collecting the Requirements
Data Warehouse Architecture
Infrastructure and Metadata
3. Creating a Data Warehousing 9
Deployment and Growth
4. Tasks of Data Mining 6
5. Statistical evaluation of data 6
5. Data preparation and data reduction 6
Segmentation, outliers, and training sets
6. Data modeling and prediction 6
Classification and Clustering
Teaching Methods (including innovative teaching methods/technology used)
• Online course management system that includes:
o online discussion board
o online collaboration
o digital dropbox
o online gradebook
o online tests
• Global and International
o The global issues are addressed through the emphasis on the importance of management decision
support systems in global and international markets.
o The ethical concerns are addressed through the emphasis on confidentiality, security, and integrity
issues in data management. In addition, online copyrights, academic honesty and integrity are
• Political, Social, Legal and Regulatory, Environmental and Technological issues are addressed through an
emphasis on their importance in management decision making.
• Demographic diversity is a fact of life on our campus. Through collaboration and interaction with each
other, students learn about demographic diversity.
• The course is about information technology and is taught using information technology
Basic Skills Coverage
• Written communication activities are used intensively
• Critical thinking skills are introduced throughout the course
• Teamwork projects will be assigned
• Library usage
o Students are encouraged to use the campus library electronic resources
(http://library.csudh.edu/FindJrnArtElecRes.php) for locating and retrieving data, information, and
scholarly papers for reports
• Computer Usage
o Students are required to prepare all reports and complete their projects using the computer and
software including data warehousing software.
• Appropriate Instructional Technology
o This course will utilize a course management system that includes discussion board, online grade
book, emails, online course materials, etc.
The Data Warehouse Lifecycle Toolkit, Ralph Kimball, Laura Reeves, Margy Ross and Warren
Thronthwaite, John Wiley & Sons, Inc., 1998. (ISBN 0-471-14931-4)
Principle of Data Mining, Hand, Mannila, and Smyth, MIT Press, Cambridge, MA, 2001.
Eyadat, Fisher, Wong