Course Policies

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Expectations:

The expectation is that all in-class activities, labs, and the semester-long project will be submitted on time, by the due date and time/time zone indicated in Canvas. Submitting your work on time ensures you learn pre-requisite skills and concepts and that you are prepared to learn the next set of skills.

Late Work Policy:

We recognize that life can get busy and you may occasionally miss a deadline. To provide some flexibility for particularly busy weeks or weeks with conflicts, you may skip one Lab assignment and one ICA with no penalty; the missed submission will simply be excluded when your grade is calculated. Beyond these waivers, any additional missed or late submissions will receive a zero. However, if something comes up, please don't hesitate to reach out — my door is always open.

Falling behind can have serious consequences. Assignments and learning activities prepare you to complete subsequent assignments, and continual lateness will impact your workload over the term and may prevent you from catching up. If you do fall behind, you should always complete the earlier assignments first.

All work must be completed by the end of the semester regardless of extensions/grace period. Work submitted after the last day of the semester will not be considered in the computation of the final grade.

Extenuating Circumstances:

If you need help, are falling behind and struggling to catch up, or if extenuating circumstances arise, the sooner you reach out for help, the more options there are to help and support you. Please contact Heather Corley, Student Success Advisor, at hcorley@virginia.edu, if you are experiencing extenuating circumstances.

Accessing Grades and Feedback in Canvas:

All assignment grades will be posted in Canvas. Most assignments will be graded within two weeks of submission. Detailed feedback on assignments will be provided, where you can view instructor and IA comments. Once grading is complete, scores will automatically sync to the Canvas gradebook.

Class Attendance:

This is an in-person course. We do not take formal attendance. However, your participation grade comes from in-class activities and labs that happen in most classes. You must be present and engaged to earn these points; late arrivals may miss activity credit. Make-ups are only available for university-approved reasons with advance notice (or as soon as practicable) and must be completed within one week. Bring your laptop, contribute respectfully, and be ready to collaborate.

Communication and Student Response Time:

Communication happens only through Piazza or email. Piazza is preferred, since email may be missed — if you post a question there, the instructor or IA will respond as soon as possible. Asking questions on Piazza, in front of others, promotes discussion and reduces the need to repeat answers. Questions containing personal information should be emailed instead, and when emailing, please put [DS1002] at the very front of the subject line. The sooner you inform the instructor of any problem that may affect your attendance or performance, the better the chance of solving it together.

Academic Integrity: Collaboration and Cheating:

Cheating tends to happen at higher rates in introductory programming-based courses because students get frustrated when their code won't run, because of the feeling that there is only one correct way to write the code, and because of how easy it is to copy and paste a few lines of someone else's code. Cheating also happens at higher rates during high pressure situations, such as a course like this one taught on a rigid timeframe.

Although every student is responsible for their own lab reports, you may chat and Zoom with one another to work together on labs. In general, the difference between collaboration and cheating comes down to intent: cheating is trying to circumvent the learning process, while collaboration is trying to help yourself and your classmates learn the material more deeply.

Examples of cheating:

• Directly copying someone else's text word for word, copying text from GenAI.
• Sharing/showing code for the purpose of circumventing the learning process (e.g., letting someone copy code because they are running up against the deadline).
• Asking for help without doing anything to try to solve the problem first; asking someone to do the work for you.
• Using AI-powered autocomplete or inline code-generation features built into the editor (e.g., GitHub Copilot, VS Code's AI code completion) that write code automatically as you type or from comments.

Things that are okay:

• Sharing/showing individual lines of code for the purpose of teaching/explaining or helping someone understand the material.
• Debugging together (only possible if both people have already written their own code, otherwise there's nothing to debug).
• Sharing strategies, external texts, blogs, and other resources for completing problems on the lab assignments.
• Deliberately using an external AI chat tool (e.g., ChatGPT, Claude) as taught in the AI for Programming unit — writing a specification first, then verifying the AI-generated code — is not cheating in that context.

Please promise on your honor to not share code or quiz answers. Cheating means that you do not give yourself the opportunity to master the skills to start working with data in Python. If you are stressed out about the intensity of the course, please message the instructor and work together to get back on track.

SDS Guidelines on AI Tools and Assistance:

The use of generative AI tools and foundation models (i.e., ChatGPT, Claude Code, Gemini, and similar tools) is permitted with the following activities, in accordance with the stated guidelines, at no penalty:

• Brainstorming and refining your ideas;
• Fine tuning your research questions;
• Finding information on your topic; and
• Checking grammar and style.

Students are responsible for:

• Acknowledging that large language models tend to produce incorrect facts and fake citations.
• Acknowledging that code generation models may produce inaccurate outputs.
• Acknowledging that image generation models can occasionally produce highly offensive content.
• Taking responsibility for any inaccurate, biased, offensive, or otherwise unethical content submitted, regardless of origin (student-generated or from a foundation model).
• Properly citing the contribution of the foundation model or other AI tools in submitted material.
• The entirety of any information they submit, based on an AI query or AI assistance.

The use of generative AI tools is NOT permitted for:

• During the ICAs and labs.
• Completing group work assigned to a student, unless it is mutually agreed upon that they may utilize the tool.
• Writing a draft of a writing assignment.
• Writing entire sentences, paragraphs or papers to complete class assignments.

Students may be penalized for:

• Using a foundation model without including an acknowledgement.
• Improperly citing the use of work by other human beings or the submission of work by other human beings as that of the student.
• Violating intellectual property laws.
• Submitting materials containing misinformation or unethical content.

The usage of AI tools must be properly cited to stay within university policies on academic honesty. Failure to adhere to these guidelines will result in a failing grade on the assignment or exam (a zero) and may be an honor code violation depending on the context (to be determined at the instructor's discretion). Having said all these disclaimers, the use of foundation models is encouraged, as it may make it possible for you to submit assignments with higher quality, in less time.

Technical Support:

• Login/Password: in.virginia.edu/helpdesk
• Canvas: Check the Help tab for resources