CSSE-386 Data Mining with Programming

Winter 2024-2025

Section 01-02

Section 1 M
T
R
F
 10:00-10:50a
10:00-10:50a
10:00-10:50a
10:00-10:50a		 O201
O201
O201
online

Section 2 M
T
R
F
 11:00-11:50a
11:00-11:50a
11:00-11:50a
11:00-11:50a		 O201
O201
O201
online

Instructor Contact Information

Dr.Olga Scrivner

Office: F204

Email: scrivner@rose-hulman.edu

Course Description and Structure

An introduction to data mining for large data sets, including data preparation, exploration, aggregation/reduction, and visualization. Elementary methods for classification, association, and cluster analysis are covered. Significant attention will be given to presenting and reporting data mining results. Students may not get credit for both this course and the MA 384 Data Mining courses.

Prerequisites

Required Materials

Textbook: CSSE 386: Data Mining with Programming ZyBook

Course Learning Outcomes

Students who complete this course should be able to extract value from large data sets. In particular, students will be able to write computer code that:

Students will be able to communicate data mining results to a wider audience.

Overview and Schedule of Assignments

For athletic or campus-wide events, please provide documentation.

Schedule

The course schedule page has the topics and due dates for the course materials. Please bookmark that page.

Reading Assignments, Worksheets, and Quizzes

Each lecture will be supplemented with paper worksheets that students will complete in class and submit to gradescope.

There will be a weekly paper quiz (open notes) in-class.

Exams

There will be no make-up exams. For exam accommodation, please provide documentation and request scheduling at least 1 week ahead.

Coding Project: Kaggle Competition

There will be no make-up exams. For exam accommodation, please provide documentation and request scheduling at least 1 week ahead.

Attendance

Attendance is required M,T,R. For athletic or campus-wide events, please provide documentation. For excused absences, please provide documentation - you also will be allowed to make-up worksheets for missing days.

Note for unexcused absences: if you missed more than 6 days (2 weeks) - you will not be allowed to take exams.

Homework

Solutions will generally be discussed next week after homework is due. You can only request up to 2 days late submission. Once the solution is discussed you are no longer will be allowed for late submission. Solutions to the homework should be presented using good style. Your name should appear at the top of each page. Be sure to state any assumptions that you make to solve the problem. Above all it must be legible--if we can't read it, you won't get credit.

Citing all external resources is crutial. While you are not allowed to solve problems with genAI, you can use fro debugging but you MUST cite the resources you used: document what wprompt was used and was solution was provided.

Labs Assignments

Lab assignments will be listed on the on the schedule. The labs will be done using notebook python environment. Completed labs are submitted via GradeScope (read instructions for each individual lab).

Live labs will be done in class and must be checked for completion. No submission on Gradescope

Grading Policy

Total: 100%

Generally, 90-100% is an A, 85-89% is a B+, etc.

The above is a guideline that we typically follow. Please understand that it is not a promise. We will do our best to conform to the institute-wide grading policy described in the Grade Descriptions section of the registrar's web page. As you read it, note in particular that phrase "thorough competence to do excellent work" appears in the description of the "B" grade (the standard for "A" is even higher), and it further states that "B" and "B+" will not be given for mere compliance with the minimum essential standards of the course.

Institute Policies

Students with Accessibility Needs: Rose-Hulman is committed to working with students who have special needs or disabilities. Visit the Accessibility Services website for more information.

Collaboration

Collaboration is encouraged on homework and laboratories. It is prohibited on exams.

When you collaborate, you must:

Working out a homework solution as a group can be acceptable collaboration if you follow the guidelines above. Each individual is responsible for understanding the entire solution. For homework, this means that once a group solution has been achieved, each collaborator must rework the problem and write up the solution independently.

If you are ever in doubt about whether some specific situation violates the policy, the best approach is to discuss it with your instructor beforehand. This is a very serious matter that we do not take lightly. Nor should you.

You should never look at another student's code to get ideas of how to write your own code. Beginning the process of producing your own solution with an electronic copy of work done by other students is never appropriate.

Use of Internet and LLMs for Class Work

Searching the Internet or other resources for answers to homework problems, labs, or exams is considered academic dishonesty. Likewise, use of Large Language Models (LLMs) such as ChatGPT to obtain answers to homework problems, labs, or exams is considered academic dishonesty.

This policy are in place because it is not helpful to your learning to use these tools in this way, and academic integrity requires that all of the work you submit is your own.

Academic Integrity and CSSE Integrity Committee Procedures 

It is critical to maintain academic integrity. It is essential for all students to cite any and all sources of help received in completing coursework. This practice not only fosters a culture of honesty and transparency but also prevents misunderstandings that might otherwise escalate to formal proceedings. Students should also be aware of what is appropriate help on homework assignments – see What Constitutes Misconduct. To ensure fairness and responsibility, any instances of suspected misconduct will be handled through the CSSE Integrity Committee. 

If a case of suspected misconduct arises, it will be submitted to the CSSE Integrity Committee for review (see Policies and Procedures and possible penalties (see IntegrityPilotPolicy). The process includes an initial review of the evidence by the committee, a time for students to explain or admit to potential misconduct, and potentially a hearing to examine the circumstances and evidence. Students are encouraged to continue their studies and engage with the course material and instructor normally throughout the investigation. 

This policy can also lead to activating the Institute Academic Integrity Policy, described here.

It is expected that any work submitted for assessment represents the intellectual work of the individual(s) submitting the work. Any attempt to pass off the intellectual work of another (including the work generated by Large Language Models like ChatGPT) as their own or without proper attribution is an example of academic misconduct and is subject to the penalties described in the Rose-Hulman Academic Rules and Procedures and Student Handbook documents.