CSCI 132 / Spring 2024

Data Structures

Instructor:

Meeting Times:

  • Lecture: Tues / Thurs 9:30 - 10:45am
    Location: Swords 328
  • Lab A: Mondays 10:00 - 12:00pm
    Location: Swords 219
  • Lab B: Mondays 2:00 - 4:00pm
    Location: Swords 219

Open Office Hours / Student Hours:

F. Mohsin
(in Swords 339 or Swords 219)
  • Wednesdays 11:00-12:00pm
  • Thursdays 2:30-4:00pm
  • or by appointment
Teaching Assistants
(in Swords 219 or Swords 3rd Floor Lounge)
  • Sunday, Mon, Tues, Wed, Thurs 7-9pm

Course Description

You know some of the basic tools of computer programming. This course will teach you to use those tools in an effective way. There are four main ideas we will be addressing in this course:

  1. Data Abstraction: In order to build programs that solve complex problems, it is important to specify the properties and behaviors of certain parts of the program independent of the implementation. We will learn about Abstract Data Types to think about specification and manipulation of these components of code.
  2. Modularity: Each piece of a program is much more useful if it can be combined with multiple other program pieces in a modular fashion. We will learn how to build program modules that can be used and reused in different programs.
  3. Algorithmic Complexity: Some solutions to problems are better than others in terms of their efficiency. Some have efficient use of memory space; others run more quickly. We will learn some tools to evaluate the efficiency of different algorithms.
  4. Standard Tools: We will learn some of the standard ways of organizing and working with data that should be a part of every programmer's toolkit. These include Stacks, Queues, Searching & Sorting algorithms, Tables, and Trees.

Prerequisites: CSCI 131 with a grade of C or higher, or permission of the instructor. You should be comfortable with the basics of programming in Java, C, C++, or related language.

Diversity and Inclusion

I intend for every student to feel their voice is welcome in this course. My goal is to create a learning environment that supports each of you and embraces the diversity that you all bring. All members of the class are expected to be respectful and supportive of each other's ideas and identities, even when they disagree. Even when discussing narrow technical matters, racism, sexism, and other hidden assumptions can appear, and there are difficult ethical dimensions to everything we do as computer scientists. I will endeavor to point these out when they appear, and to check my own assumptions about your past experiences and perspectives. We all make mistakes that can impact others. but I hope that by committing to listen reflectively, and without defensiveness, we can build a strong and inclusive community. Please let me know if there is anything in class meetings, in office hours, in study sessions, or elsewhere that troubles you or makes you feel uncomfortable or unvalued. Even for events outside of the classroom, I welcome and appreciate any suggestions you have pertaining to diversity, equity, and inclusion.

If you prefer to speak with someone outside the course, please do not hesitate to reach out to any other faculty, the class deans, department members of the student advisory council, or others you feel more comfortable speaking with.

Also, if you have a name and/or set of pronouns that differ from those that appear on my class roster, please let me know!

Textbook

F. Carrano and T. Henry.
Data Abstraction & Problem Solving with C++: Walls and Mirrors,
7th Edition, Pearson, 2017.
This is a required textbook.

Grading & Assignments

Grades will be computed approximately as follows:

Projects20%
Labs10%
Quizzes10%
Midterm Exam 120%
Midterm Exam 220%
Final Exam20%

Projects There will be about eight individual programming assignments, each requiring programming in C++. Each is due on the assigned due date. However, generally projects are accepted up to three days late, but the maximum possible score for an assignment will be reduced by 10% for each day or portion of a day that the assignment is turned in late. So the maximum possible score for an assignment turned in up to 24 hours late is 90%, and the maximum possible score for an assignment turned in up to 48 hours late is 80%, and so on. Late work will not be accepted after graded assignments are returned or solutions are discussed in class. Some assignments have more strict late penalties, so that solutions may be discussed sooner in class, e.g. before an exam.

Labs There will be about eleven lab assignments to be completed in the two-hour weekly lab sessions. Attendance in lab is mandatory. Lab assignments are designed and intended to be completed by the end of the lab session, but will be accepted without penalty by the due date listed on the assignments page, generally at 11PM on the following Friday. Labs will not be accepted after this extended due date. One lab score will be dropped. This means you may miss one lab for any reason or no reason at all, without penalty. Or, if you complete all the labs, then your lowest lab score will be dropped. However, zero or low score due to academic integrity violations will never be dropped.

Quizzes There will be approximately eight short in-class quizzes. Quizzes might be held either in class or lab. Quizzes cannot be made up except for cases outlined by the College as excused absences and with prior notification (e.g., for recognized religious observances and athletic competition). One quiz score will be dropped. This means you may miss one quiz for any reason or no reason at all, without penalty. Or, if you complete all the quizzes, then your lowest quiz score will be dropped. However, zero or low score due to academic integrity violations will never be dropped.

Exams There will be two miderm exams, held outside of class time, and a comprehensive final exam during the final exam week at the end of the semester. These are are tentatively scheduled:

Discussion Log & Collaboration Policy

Please refer to the Math and CS Department Honor Code Policy and the College Academic Integrity Policy.

Moderate Collaboration for Lab Assignments Moderate collaboration is allowed, expected, and encouraged on lab assignments. This means you may talk to your fellow students about how to go about solving the various problems presented. During our (online) labs, we expect you to share screens, help debug each others code, and solve problems together. However, you should type in and submit your own solution, even if you worked together. No deduction is taken for this moderate collaboration, so long as you list your partners in your collaboration log. Lack of acknowledgement in your collaboration log is considered a serious breach of conduct.

Limited Collaboration for Programming Projects Only limited collaboration is allowed on programming projects. This means you should limit your discussion with your fellow students (or others) to general information and examples. You may discuss the logic for solving a programming problem and the meaning of compiler error messages with other students, for example, or discuss a tricky piece of syntax or line or two of code. But you may not discuss larger pieces of program code with other students. A good check for this is that you should be able to speak mostly in plain English when discussing assignments, and you should not share files or copy-paste code with another student. You may not work with another student to write a piece of code and have each student turn in that code as if it was their own. You may not copy the file of another student (or any other source) and turn it in as your own work.

A typed discussion log is required for each and every assignment. No work will be accepted without a written discussion log.

In general, you may refer to your texts, your class notes, and your course instructor for help. You may also consult public literature (books, articles, web sites) for general information and examples, but you should not seek or use published solutions to assignments. You may also talk with with fellow students about general information and strategies for solving assignments, but not specific solutions or code. The work you turn in must be your own. If you are in doubt about what is acceptable, be sure to ask the course instructor.

Clarification about Artificial Intelligence or "generative AI": Generative AI models like ChatGPT, BARD, GitHub/Microsoft Copilot, or similar code generation tools are clearly useful. And they may turn out to be helpful for learning, or maybe they will prove to be unhelpful in long run. Honestly it's probably too early to tell. For the purposes of CSCI 132 and the collaboration policy, you should treat generative AI models as if it were a person -- say, a very well-read, sometimes clever, and very confident but sometimes unreliable roommate. That means it's okay to ask a model for general help understanding class material (and cross your fingers it doesn't "hallucinate"). But it's not okay to put homework questions into a model, or to ask the model to solve specific tasks that an assignment has tasked _you_ to perform. That crosses the line into simply cheating, just as asking a roommate to do your homework would be a violation of the College academic integrity policy. If you consult or use generative AI in any of your assigned work, you must cite the specific tools you used and provide a list of all prompts you used in your discussion log. You are still responsible for ensuring the correctness and accuracy of all submitted work. In addition, you are responsible for ensuring that all source materials used in your work are properly cited--you should be aware that generative AI can often produce output copied closely (or sometimes directly) from source material without properly citing those sources. Failure to correctly and fully cite sources constitutes plagiarism and is a violoation of the academic integrity policy.

Clarification about other online sources: It is fine to use Google and other sites to look for snippets of publicly available code that might help you with assignments, and it is okay to use a limited amount of such code in your own work. You should not take entire solutions or large amounts of code from the web. And you must clearly comment your code to indicate which code and ideas are purely your own, which code or ideas are borrowed or adapted from elsewhere, and where the other code or ideas came from.

In all cases you must cite each source of ideas you adopt in your discussion log for each assignment. You should never present another person's work as your own. By clearly indicating any sources you consult and the people with whom you collaborate, you are giving credit where it is due. If you borrow or adapt code, and if the code is good, then you will get some credit for having found it (you won't get credit for writing it, since you didn't). If you borrow bad code, the fault is all yours. If you borrow or adapt code or use source material but don't cite it, that's plagiarism, and will be considered a violation of the academic integrity policy.

Discussion Log

For each assignment, you must keep a log detailing every collaboration you had with someone else and every source you consulted when completing the assignment. Each log entry must include: the date and time, the source, the length of time spent talking or reading, and a summary of discussion or material read. If you consult or use any form of generative AI while completing an assignment, include the specific AI system used and list all prompts used. You don't need to include the course textbook, the lecture notes, or the instructor, since it is assumed you will consult these sources.

Here is an example—you should use a similar format:

Even if you did not discuss anything with anybody and never consulted any other sources, you must still submit a discussion log that says just that, like so:

Failure to submit a complete discussion log will result in a penalty.

Attendance and Excused Absences

Enrolled students are expected to attend all regular class meetings and to participate in discussions and other class activities, except for excused absenses or by arrangement with the instructor. Please refer to the College Attendance and Excused Absence Policies.

Class Recordings

Consistent with applicable federal and state law, this course may be video/audio recorded as an accommodation only with permission from the Office of Accessibility Services.

Reasonable Accommodations and Accessibility Services

The instructor is committed to providing students with disabilities equal access to the educational opportunities associated with this course. For details or to request accommodation, please refer to College procedures on Requests for Reasonable Accommodations and the Office of Accessibility Services.