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  1. 1. 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 processes: 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
  2. 2. 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. Grading: 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. Grading Scale 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 Course Content Hours Coverage 1. Introduction 3 Basic Elements of the Data Warehouse 2. Principles of Data Warehousing 9 The Business Dimensional Lifecycle
  3. 3. Project Planning and Management Collecting the Requirements Dimensional Modeling Data Warehouse Architecture Infrastructure and Metadata 3. Creating a Data Warehousing 9 Aggregation Data Staging User Applications Deployment and Growth 4. Tasks of Data Mining 6 5. Statistical evaluation of data 6 Statistical Models Estimation 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 email o online tests o etc Perspectives Coverage • 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. • Ethics 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 addressed. • 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 Instructional Resources
  4. 4. • Library usage o Students are encouraged to use the campus library electronic resources ( 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. Textbooks 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. Faculty Eyadat, Fisher, Wong