Data Structures (S2022)overview | logistics | schedule | labs | presentations | resources
Lecture: Mon & Thu 1:30-2:50pm (RKC 103)
Lab A: Fri 2:00-4:00pm (RKC 100)
Lab B: Fri 9:50-11:50am (RKC 100)
KO Office Hours: Mon 11am-12pm, Thu 10-11am, and by appointment (RKC 204)
KN Office Hours: Mon & Thu 3-4pm, and by appointment (RKC 206)
- Algorithms. Sedgewick and Wayne. 4th Edition. 2011. Pearson.
- Exams (60%): Three closed book exams
- Labs (20%): Weekly programming assignments
- Participation (10%): Homework, quizzes, discussion (almost every class)
- Presentation (10%): Presentation of an advanced data structure
- Participate. There will be myriad opportunities: in RKC 103/7, google docs, repl.it, dropbox paper, excalidraw, office hours, discord.
- When reading, studying, and listening, be active by taking notes and asking questions.
- When reading small code examples: read, run-in-your-head, run-on-the-computer & riff-and-revise.
- Visit the professor's and tutor's drop-in hours.
- Attend class & be on time (whenever possible given COVID reality).
- Make sure to have read the required reading BEFORE class.
- Start all the assignments early.
- Check Google Classroom & this class website.
- Be respectful of your fellow classmates; my rule of thumb for judging whether a response is worthwhile: Is it Nice? Is it True? Is it Necessary? Pick at least two.
- Adhere to the Code of Ethics and Professional Conduct for the Association for Computing Machinery.
- Cooperate carefully and thoughtfully:
- Work within your pair & pod, and visit drop-in hours, before seeking help beyond.
- Credit work, including all sources you used from the web, other books, etc.
- Sharing ideas is encouraged, but blatantly copying work without attribution will be treated as scholastic dishonesty and receive no credit.
- Be prepared to demonstrate the theory of your program (Peter Naur).
- Keep your work backed-up and private using Google drive.