CSCI 373: Advanced Databases
Description	This course will provide pratical coverage of essential data mining and data warehousing topics including: Data mining inputs and outputs Data mining algorithms Evaluating data mining results Data warehouse models and OLAP operations Issues in data warehouse design such as monitoring, integration, data cleaning, data loading, and metadata. Students will work with the Weka data mining tool set version 3.4 (http://www.cs.waikato.ac.nz/ml/weka/). The course will also include work with GIS databases and distributed databases.
Text	Data Mining: Practical Machine Learning Tools and Techniques (Second Edition) by Ian H. Witten and Eibe Frank. Morgan Kaufmann, 2005.
Instructors	Rebecca Bruce Office: RBH 024 Telephone: 232-2275 e-mail: bruce@cs.unca.edu Office Hours: TBA   Joe Brownsmith Office: RBH 220 Telephone: 232-5046 e-mail: brownsmith@cs.unca.edu Office Hours: TBA

Resources:

Grades

Grades will be based on points earned from assignments frequent quizzes, and a final exam as follows:

The following numerical scale will be used in assigning letter grades based on Score, the weighted score computed using the preceding table. The instructor reserves the option of relaxing the cut-offs for a letter grade in special circumstances.

Grading Procedures:

Assignments: Assignments will be graded on the +/ok/- scale, where + indicates excellent, ok indicates satisfactory, and - indicates needs improvement. Assignments will be given each class period and will be due at the start of the following class period; late assignments will not be accepted. Your solutions to these assignments are your way of telling the instructor about your mastery of this course. Your solutions must be clearly different than those turned in by others in the class and represent a unique and special effort on your part.

Quizes: During the semester there will be seven short minute quizzes. These quizzes will cover recent class material and will resemble homework questions. Quizes will be given on the dates indicated unless otherwise indicated in class. Your lowest quize grade will be droped in calculating your course grade.

Attendance Policy:

Lectures: Students are expected to attend all class lectures. Failure to do so will be will impact your homework grade and will be considered a lack of interest in success on the part of the student.

Quizes: If you must miss an quiz due to illness you must email or telephone the instructor before the scheduled time and perhaps something can be arranged to avoid a zero for this quiz. Failure to notify the instructor prior to the scheduled time will produce an automatic zero for the quiz.