This course provides students with the key concepts and tools to turn raw data into useful intelligence. A broad spectrum of business situations will be considered for which the tools of classical statistics and modern data mining have proven their usefulness. The course covers data mining techniques, their application and their usage. Data mining software is used extensively in this course. (same as MPA671)
MGT510 or concurrent
Upon completion of this course, students will be able to:
Specified on the course schedule/outline
| Letter Grade | Range | Definition |
|---|---|---|
| A | 90-100 | Excellent |
| B | 80-89 | Above Average |
| C | 70-79 | Average |
| D | 60-69 | Below Average |
| F | 0-59 | Failing |
| 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