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Data Warehouse and Mining CSIT 473 Instructor: Dr. Natalie Nazarenko Office: Fenton Hall 2137 Hours: 8:00 - 9:30 am; 4:00 – 5:00 pm MWF, and by appointment Phone: (716) 673-4684 E-mail: [email protected] Text: Data Mining: Concepts and Techniques, Jiawei Han and Micheline Kamber, Morgan Kaufmann Description: As an introductory course on data mining, this course introduces the concepts, algorithms, techniques, and systems of data warehousing and data mining, including (1) data preprocessing, (2) design and implementation of data warehouse and OLAP systems, (3) systems, architectures and algorithms for effective and scalable data mining, including frequent pattern and correlation analysis, classification and cluster analysis. The course will serve mainly senior-level computer science undergraduate students and the first-year graduate students interested in the field. Also, the course may attract students from other disciplines who need to implement and/or use data warehouse and data mining systems to analyze large amounts of data. This course will draw materials mainly from the textbook. Students will study the materials and complete all the course requirements. Prerequisites: CSIT351. Web Sites: https://fredonia.sln.suny.edu/default.asp www.cs.fredonia.edu/nazarenko Attendance: You are expected to attend all lectures. Exams must be taken on the date assigned unless prior arrangements have been made. Make-up exams will not be given except in fully documented cases of medical or other emergencies. Please make sure that you can attend all the exams Grading: 1) Homework 40% 2) Tests, Quizzes 40% 3) Final test 20% Grading Scale: 94%100% A 90%93% A- 88%89% B+ 82%87% B 80%81% B- 78%79% C+ 72%77% C 70%71% C- 68%69% D+ 62%67% D Newsgroup: Announcements will be posted to the newsgroup and our Web site. Make sure to check the newsgroup frequently enough to stay informed. Use the class newsgroup for your questions. As everyone can see them, it is more likely to get quick response. You are encouraged to answer or participate in the discussions for questions posted on the newsgroup. However, there are obviously things that are not appropriate for the newsgroup, such as solutions for assignments as well as comments or requests to the staff. Assignments/Projects: All assignments/projects are due at the beginning of class on the dates to be set by the instructor. A 10% penalty will be assessed for each calendar day of lateness. Assignments/projects handed in more than one week (seven days) late will not be accepted for credit. These assignments and projects should be done with your own efforts. All parties involved in copying a given assignment shall get grade divided by number of participants. You should spend at least three hours per week outside of class to complete the assignments. Academic Honesty: The specific actions in response to incidents of student dishonesty are inclusive of receiving a failing grade for an exam, quiz, assignment or the course, suspension or dismissal from college. I grades are given only if illness, hardship or any other extra 60%61% D- 0%59% Fail ordinary circumstances preclude the completion of the course. An I grade must be arranged before the final examination. Examination, quiz dates and assignment due dates will be announced in class. This syllabus may be subject to change.