Corporations today are said to be data rich but information poor. Data mining techniques can help companies discover knowledge and acquire business intelligence from these massive data sets. This course will cover data mining for business intelligence. Data mining refers to extracting or “mining” knowledge from large amounts of data. It consists of several techniques that aim at discovering rich and interesting patterns that can bring value or “business intelligence” to organizations. Examples of such patterns include fraud detection, consumer behavior, and credit approval. The course will cover the most important data mining techniques - classification, clustering, association rule mining, visualization, prediction - through a hands-on approach.
Upon completion of this course, students will be able to:
Specified on the course schedule/outline
|W||—||Withdrawal during weeks 1 - 7|
|WF||—||Withdrawal failing after week 7|
|NF||—||Failing – Not actively engaged|
For more details about the Grading System, please see the current catalog.
Students must be actively engaged in the course. For a definition of active engagement, please see the current catalog.
Cheating and plagiarism are serious offenses against the University’s academic integrity and are consequently strictly prohibited. All students must familiarize themselves with the University policy on Academic Integrity.
Penalties for cheating and plagiarism are described in the University policy on Academic Integrity in the catalog. They include failure of the assignment, failure for the course, or dismissal from the University. For the complete Cheating/Plagiarism policy, please see the current catalog.
Students who have disabilities that may impact their performance in this course should follow the process described under the heading Accommodations for the Disabled in the current catalog.
Date of last review: Unknown