CISC 4631 R01 Data Mining (Fall 2018)
Class Time: Mon,Thu 11:30-12:45 PM
Classroom: Keating Hall 312
Instructor: Prof. Truong Huy Nguyen (firstname.lastname@example.org)
Office Hours: Thu 2:00-3:30 PM or by appointment via email
Office (Rose Hill): JMH 402C
This course covers the methods, algorithms, and applications of data mining. Topics include: introduction of data mining, data processing; data mining algorithms and techniques (e.g. association rules, clustering, and classification); metrics and methods for model evaluation, as well as post-process visualization of results. After taking this course, the students should develop an understanding and familiarity with data analysis and data mining algorithms, know under which circumstances they are applicable, and be able to apply them to solve real-world problems. Each student will also select and complete an application-oriented or research-oriented course project.
4.00 Credit Hours.
After completion of this course, the student will:
- Develop a basic understanding of data mining, allowing them to recognize what problems can be addressed by data mining and which data mining methods are most appropriate for a given task.
- Gain experience with data mining tools and methods, including some experience with applying them to real world data sets and real world problems.
Required: "Data Mining: Concepts and Techniques, 3rd edition", by Jiawei Han, Micheline Kamber, and Jian Pei. Morgan Kaufmann, 2011 (ISBN: 978-9380931913).
(Online book) "R for Data Science", by Garrett Grolemund and Hadley Wickham. O'Reilly Media, 2017.
The percentages given below are guidelines for both students and instructor; minor changes may be made during the course (students will be informed promptly of any such changes).
Attendance, participation 11% Homework assignments (4) 24% (6% each) Midterm Exam 20% Course Project 20% (Proposal: 5%, Final Report+Presentation: 15%) Final Exam 25%
To map a numerical grade to a letter grade, I use the following mapping (which is the default built into Blackboard). However, in some cases I may curve grades upward.
A 94-100 C+ 77-80 A- 90-94 C 74-77 B+ 87-90 C- 70-74 B 84-87 D 65-70 B- 80-84 F <65
Late Submission Policy
Assignments must be completed by the date specified in their respective descriptions and submitted according to the instructions provided by me. There will be a 20% point deduction each day after the due date. Assignment will not be accepted 3 days past the due date, unless under extraordinary circumstances and priorly approved by the instructor.
The course will include a course project. You may work individually or in teams of 2 (special permission is needed to work in larger teams). You may address a research question or analyze a real world data set. Consider working on a project that relates to a hobby or interest of yours. A good start is to try to find high quality data-- once you have a data set you can often find a data mining problem related to it. You are responsible for coming up with your project topic but I can help you if you are having trouble.
Academic integrity is very important to the mission of the university. Plagiarism or failure to properly cite sources will result in an F on the assignment and may result in an F for the course. You are responsible for and expected to follow the Fordham College at Rose Hill policy regarding matters of academic integrity.
If you are a student with a documented disability and require academic accommodations, please register with the Office of Disability Services for Students (ODS) in order to request academic accommodations for your courses. Please contact the main ODS number at 718-817-0655 to arrange services. Accommodations are not retroactive, so you need to register with ODS prior to receiving your accommodations. Please see me after class or during office hours if you have questions or would like to submit your academic accommodation letter to me if you have previously registered for accommodations.