CSI 5355: Data Mining and Analysis, Fall 2026
Objectives
Practical information
Lectures are Tuesdays and Thursdays 09:30-10:15 in room Cashion C315.
My office hours are listed on my home page. I am glad to talk to students during and outside of office hours. If you can't come to my office hour, please email me to make an appointment at another time.
All course content will be available on Canvas
Schedule
Please consult the schedule on Canvas.The final exam will according to the university schedule. For the latest university finals information, check the link labeled "Final Exam Schedule" from the Baylor Registrar Page. Make sure you are looking at the schedule for the current semester.
Textbooks & resources
This semester we will use Introduction to Data Mining (Second Edition) by Tan, Steinbach, Karpatne, and Kumar. You are expected to have and read a copy of the textbook.
We may also be reading from some papers in the research literature.
Grading
Grades will be assigned based on this breakdown:
- assignments: 10%
- semester project: 30%
- midterm exam: 30%
- final exam: 30%
Here is the grading scale:
F <
60 ≤ D- <
62 ≤ D <
67 ≤ D+ <
70 ≤ C- <
72 ≤ C <
78 ≤ C+ <
80 ≤ B- <
82 ≤ B <
88 ≤ B+ <
90 ≤ A- <
92 ≤ A
Some assignments may be worth more than others. Exams are closed-book. The final will be comprehensive.
Policies
- Check this website and your email often for updates and announcements. We only meet a few times a week, but I may post updates at any time. It is your responsibility to follow these updates.
- All work in this course is strictly individual, unless the instructor explicitly states otherwise. While discussion of course material is encouraged, collaboration on any work for the course is not allowed. Collaboration includes (but is not limited to) discussing with anyone other than the professor any material that is specific to completing an assignment. You are encouraged to discuss the course material with the professor, preferably in office hours, and also by email.
- Assignments which are late are not accepted without prior arrangement. Exams may be made up with prior arrangement (made at least one class before to the exam) or due to illness, with a note from a health care professional.
- Bring any grading correction requests to my attention within 2 weeks of receiving the grade or before the end of the semester, whichever comes first. After that, I will not adjust your grade. If you find any mistake in grading, please let me know.
Academic honesty
I take academic honesty very seriously.
Many studies, including one by Sheilah Maramark and Mindi Barth Maline have suggested that "some students cheat because of ignorance, uncertainty, or confusion regarding what behaviors constitute dishonesty" (Maramark and Maline, Issues in Education: Academic Dishonesty Among College Students, U.S. Department of Education, Office of Research, August 1993, page 5). In an effort to reduce misunderstandings in this course, a minimal list of activities that will be considered cheating have been listed below.
- Copying another student's work. Simply looking over someone else's source code is copying.
- Providing your work for another student to copy.
- Collaboration on any assignment, unless the work is explicitly given as collaborative work.
- Using notes or books during any exam.
- Giving another student answers during an exam.
- Reviewing a stolen copy of an exam.
- Plagiarism.
- Studying tests or using assignments from previous semesters.
- Providing someone with tests or assignments from previous semesters.
- Taking an exam for someone else.
- Turning in someone else's work as your own work.
- Studying a copy of an exam prior to taking a make-up exam.
- Providing a copy of an exam to someone who is going to take a make-up exam.
- Giving test questions to students in another class.
- Reviewing previous copies of the instructor's tests without permission from the instructor.
With regard to use of AI tools, all assignments should be your original work and should not be produced in part or in total with the assistance of artificial intelligence (for example, ChatGPT, Grammarly, Gemini, or some other resource). Use of artificial intelligence without my explicit permission constitutes a violation of the Honor Code at Baylor University.
The goal of this course is that you understand the material in it. Using shortcuts like AI may be able to help get you a solution to a particular assignment, but that violates the honor code and damages your understanding. When you have questions, ask your instructor, not AI.
Title IX Office
Baylor University does not discriminate on the basis of sex or gender in any of its education or employment programs and activities, and it does not tolerate discrimination or harassment on the basis of sex or gender. This policy prohibits sexual and gender-based harassment, sexual assault, sexual exploitation, stalking, intimate partner violence, and retaliation (collectively referred to as prohibited conduct). For more information on how to report, or to learn more about our policy and process, please visit www.baylor.edu/titleix. You may also contact the Title IX office directly by phone, (254) 710-8454, or email, TitleIX_Coordinator@baylor.edu.
Copyright © Greg Hamerly, with some content
taken from a syllabus by Jeff Donahoo.
Computer Science Department
Baylor University