CISC220 F2024

Course information

Description	CISC 220 -- Data Structures Comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation. Topics include recursion, stacks, queues, lists, heaps, hash tables, search trees, sorting, and graphs.
Requirements	This is a course for undergraduates who have obstained a grade of C- or better in CISC 181, and have taken or are currently taking CISC 210 and MATH 241.
Instructor	Christopher Rasmussen E-mail: cer@cis.udel.edu Office: Smith 446 Office hours: Wednesdays, 2-4 pm in Smith 211
URL	http://nameless.cis.udel.edu/class_wiki/index.php/CISC220_F2024
TAs	Yifan Zhang, E-mail: eyfzh@udel.edu, ?? lab, office hours ?? 4-5 pm in Smith 203  ??, E-mail: ??, Thursday lab, office hours Tuesdays 3-4 pm in Smith 203
Schedule	Lectures: Tuesdays and Thursdays 9:35 am to 10:55 am in BROWN 116 Labs: Wednesdays 4:10 to 5:05 pm and 5:20 to 6:15 pm in BROWN 116 and Fridays 3:00 to 3:55 pm in BROWN 205. In the schedule below note that there is NOT a lab every week
Grading	50% Labs (5% each). These are problem sets/smaller programming exercises which are assigned in lab most weeks and due by the beginning of class each Thursday before the next lab. All written answers must be in PDF form. Attendance at labs is expected if you have not yet submitted--this is your chance to ask questions face to face and get started early on the assignment 20% Quizzes (5% each) 15% Midterm 15% Final (essentially a midterm for the second half of the course) Your labs and programming projects are due by midnight of the deadline day (with a small grace period afterward). All should be submitted directly to Canvas--e-mail submissions will not be accepted. A late homework is a 0 without a valid prior excuse. To give you a little flexibility, you have 6 "late days" to use over the semester to extend the deadline by one day each without penalty. No more than two late days may be used per assignment. Late days will automatically be subtracted, but as a courtesy please notify the instructor and TA in an e-mail of your intention to use them before the deadline. For the overall course grade, a preliminary absolute mark will be assigned to each student based on the percentage of the total possible points they earn according to the standard formula: A = 90-100, B = 80-90, C = 70-80, etc., with +'s and -'s given for the upper and lower third of each range, respectively. Based on the distribution of preliminary grades for all students (i.e., "the curve"), the instructor may increase these grades monotonically to calculate final grades. This means that your final grade can't be lower than your preliminary grade, and your final grade won't be higher than that of anyone who had a higher preliminary grade. I will try to keep you informed about your standing throughout the semester. If you have any questions about grading or expectations at any time, please feel free to ask me.
Textbook	Data Structures and Algorithms in C++ (4th ed.), Adam Drozdek. It is NOT at the textbook store (at least not new). Suggested sources: Amazon $10+ used, $33 paperback Google Play e-book $40 rental, $70 purchase Code examples from the book can be downloaded here
Collaboration and AI policy	Students can discuss problems with one another in general terms, but must work independently on all assignments except when pairs or teams are permitted. This also applies to online and printed resources, including search engine results and discussion forums: you may consult them as references (as long as you cite them), but the words (i.e., code) you turn in must be yours alone. Any quoting must be clear and appropriately cited. The University's policies on academic dishonesty are set forth in the student code of conduct here. On certain assignments where the instructions explicitly grant permission, students may use generative AI tools such as OpenAI's ChatGPT, GitHub's Copilot, Meta's Code Llama, etc. for code creation, modification, and/or bug-finding. Where no such permission is granted or nothing is said, the default assumption is that all code written originally came from and was fixed by your own human brain. Furthermore, if and when you use an AI tool for any permitted purpose, it MUST be acknowledged with a citation along the lines of these guidelines (i.e., specific tool, date, prompt or prompts used, as well as any other useful context). Such citations should be added as comments to any code files which contain AI-generated code, and a README file with all such citations should be included with any homework submission. AI tools are generally acceptable for tutorial or explanatory purposes while working on programming assignments or labs, or when studying for quizzes/exams. However, AI or search tool usage during any in-class quiz or exam is prohibited.