An investigation of data warehousing, data mining and decision support systems. Topics include design and architectural issues, cost effectiveness, management concerns, data integrity, deployment and maintenance issues. Prerequisite: CISS 430.
Prerequisite(s) / Corequisite(s):
Course Rotation for Day Program:
Most current editions of the following:
Decision Support Systems and Intelligent Systems
By Turban, E., et. al. (Prentice Hall) Recommended
Data Mining: Concepts and Techniques
By Han, J., and Kamber. M. (Morgan Kaufmann) Recommended
To learn data mining and decision support fundamentals and techniques.
To understand data collection, cleaning and aggregation issues.
To construct meaningful multi-dimensional models.
To investigate data warehousing issues.
To utilize a data mining query language.
To learn statistical techniques for analyzing data.
To utilize decision trees in data analysis.
To investigate cluster analysis.
Characterize a data warehouse.
Describe data warehousing architectures.
Discuss data clearing and reduction issues.
Utilize a data mining query language.
Employ statistical measures in data analysis.
Explain data mining association rules.
Utilize decision tree techniques for data analysis.
Perform cluster analysis.
Data warehousing techniques and issues
Query languages for data mining
Developing mining association rules
Data classification and prediction techniques
Ethical issues in data mining
Recommended maximum class size for this course: 20
NOTE: The intention of this master course syllabus is to provide an outline of the contents of this course, as specified by
the faculty of Columbia College, regardless of who teaches the course, when it is taught, or where it is taught. Faculty members teaching this
course for Columbia College are expected to facilitate learning pursuant to the course objectives and cover the subjects listed in the topical
outline. However, instructors are also encouraged to cover additional topics of interest so long as those topics are relevant to the course's
subject. The master syllabus is, therefore, prescriptive in nature but also allows for a diversity of individual approaches to course material.