ITCS 6162 Knowledge Discovery in Databases - Spring 2022 ( Sec 001 ) - ONLINE

Instructor: Dr. Angelina A Tzacheva, Department of Computer Science, College of Computing and Informatics,
Office: 435A Woodward, Office Hours: Tuesday 3:00pm - 5:00pm Email: aatzache@uncc.edu

Join Zoom Meeting
https://us02web.zoom.us/j/87995946159?pwd=NUdna25EMU5uL1FkTVdwek14MFZUZz09

SkypeID:    angelina.tzacheva

Teaching Assistants:

1. Nikhitha Modugu,  Email: nmodugu@uncc.edu
OfficeHours: Wednesday 10:00 AM - 11:30 PM & Thursday 10:00AM-11:30PM via 
  Zoom links :

OfficeHoursDay1: Wednesday 10:00 AM - 11:30 PM

ZoomLink_OfficeHoursDay1:
https://uncc.zoom.us/j/3044211437

OfficeHoursDay2: Thursday 10:00AM-11:30PM

ZoomLink_OfficeHoursDay2:
https://uncc.zoom.us/j/3044211437

SkypeID: live:.cid.fe97b230ceb66342

2. Kamalapriya Srinivasan,  Email: ksriniv2@uncc.edu

OfficeHours: Monday 12:00PM-1:30PM & Tuesday 12:00PM-1:30PM via 
  Zoom links :

OfficeHoursDay1:Monday 12:00PM-1:30PM

ZoomLink_OfficeHoursDay1:

https://uncc.zoom.us/j/92009134530

OfficeHoursDay2: Tuesday 12:00PM-1:30PM

ZoomLink_OfficeHoursDay2:

https://uncc.zoom.us/j/92009134530

SkypeID: live:.cid.c20e8d32014e341f

3. Avyay Yennamaneni,  Email: ayennama@uncc.edu

OfficeHours: Tuesday 11:30AM-1PM & Friday 10:30AM-12PM via 
  Zoom links :

OfficeHoursDay1:Tuesday 11:30AM-1PM

ZoomLink_OfficeHoursDay1:

https://uncc.zoom.us/j/93025750956

OfficeHoursDay2: Friday 10:30AM-12PM

ZoomLink_OfficeHoursDay2:

https://uncc.zoom.us/j/93025750956

SkypeID: https://join.skype.com/invite/rRUCEh8J7X0q

Prerequisites: ITCS 6160 Database Systems

Textbook:
"Introduction to Data Mining (2nd Edition)" by Pang-Ning Tan, Michael Steinbauch, Anuj Karpatne and Vipin Kumar, 2018. ISBN-10: 0133128903

Course Outline:
- Knowledge discovery process
- Types of Data, Pre-processing, Distance Measures
- Association rules discovery methods
- Action rules discovery
- Discretization algorithms
- Decision Trees
- Classification methods
- Clustering Analysis
- RSES, LERS, WEKA, ORANGE
- Hadoop, MapReduce, and distributed data mining
- Application is specific domain (health, financial, education, music)

Student Learning Outcomes:
1. Demonstrate Knowledge of Data Mining Techniques, including Granularity Based Approaches, for Incomplete Data
2. Actively Manage and Participate in Data Mining Projects
3. Extract Actionable Knowledge from Data and use it build Recommender Systems

Instructional Method: 
This is an Online course which includes Video Lectures, Reading Assignments, Exercises, Group Activities, and a Group Project.
Lectures Notes, Videos, and Reading Assignments are posted in the syllabus table below, as well as on Canvas. Please download and read each lecture material and view each Video on the specified day.
All material by date is listed, including preparation for the exams with sample questions. The Exams are open-book / open-notes. The textbook is necessary, as exam questions are based on lecture notes AND on the text, and Exercises are assigned from the textbook.

Credit Hours: This is a 3-credit hour course.
This course is designed to require about 10 hours per week - for readings, exams, exercises, video cases, and group project work.
The material is technical and requires dedication of time to comprehend.  To complete course successfully, Please do not plan on   cramming all lectures the day before the exam. Designate 3 hours every lecture day for reading the given lecture, and book chapter. Designate additional 4 hours per week for Exercises, videocase assignments, and Group meetings / activities. You can meet with your Group Members ONLINE through video conferencing - via Skype, Google Hangout, or meet in person if desired. Students are expected to communicate and meet with their group members to complete the project successfully.
Exercises are assigned after each chapter. The Exercises are due on Canvas on the dates they are assigned. Exercises are *not accepted* through e-mail. Late Exercises are not accepted.

Grading:
The final course grade is determined on the following weights:
Exercises   20%
Group Activities 15%
Midterm Exam   17%
Group Project   20%
Final Exam   18%
Video Cases 12%

Grading scale:
A   90% - 100%
B   80% - 89%
C   70% - 79%
D   60% - 69%
F   less than 60%
X   academic dishonesty

Academic Integrity and Honesty:
Students are required to read and abide by the Code of Student Academic Integrity available from Dean of Students Office. This code forbids cheating, fabrication or falsification of information, multiple submissions of academic work, plagiarism (including viewing others work without instructor permission), abuse of academic materials, and complicity of academic dishonesty. Violations of the Code of Student Academic Integrity, including plagiarism, result in disciplinary action as provided by the Code.

Civility:
We are concerned with a positive learning experience. This course strives to create an inclusive academic climate in which the dignity of all individuals is respected and maintained. We value diversity that is beneficial to both employers and society at large. Students are encouraged to actively and appropriately share their views in class discussions.

Inclement Weather:
University Policy Statement #13 states the University is open unless the Chancellor announces that the University is closed. In the event of inclement weather, check your e-mail, and Canvas. The instructor will post a message on Canvas, and through e-mail. The instructor will use their best judgment as to whether class should be held.

Disability:
UNC Charlotte is committed to access to education. If you have a disability and need academic accommodations, please provide a letter of accommodation from Disability Services early in the semester. For more information on accommodations, contact the Office of Disability Services.

Withdrawal:
The University policy on Course Withdrawal allows students a limited number of opportunities available to withdraw from courses. There are financial and academic consequences that may result from course withdrawal. If a student is concerned about his / her ability to succeed in this course, it is important to make an appointment to speak with the instructor as soon as possible.

Syllabus Revision:
The instructor may modify the class schedule and syllabus during the course of the semester. For example - additional educational videos may be posted. Same changed will appear on Canvas. Students are responsible for refreshing their syllabus once per week.

E-Mail Communication:
Students are responsible for *all* announcements made in class and on the class online resources. Students should check the online class resources throughout the semester. The Instructor and Teaching Assistants send occasional e-mails with important information. We send this information to the student's university e-mail address listed on Banner system.

Class Expectation:
By attending class beyond the first week, students agree to follow the framework and rules related to this course as described above.

Syllabus:

Syllabus Copyright 2015-2025 Angelina A Tzacheva.
No re-usage or reproduction without permission.