Comp Sci 214 — Data Structures

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Comp Sci 214 — Data Structures 

1 Course Description 

Comp Sci 214 teaches the design, implementation, analysis, and proper application of abstract data types, data structures, and their associated algorithms. We will explore a wide variety of data structures both conceptually and concretely via implementation. The class involves a significant hands-on programming component; you will both implement and use several data structures yourself. 

2 Course Staff 

Instructor 

Prof. Vincent St-Amour ([email protected]) (Call me Vincent) 

Teaching Assistant 

Leif Rasmussen ([email protected]) 

Peer mentors 

Alison Bai, Alivia Wynn, Asher Martin-Rosenthal, Blake Hu, Chris Song, Claire Paré, Ethan Pineda, Jackie Lin, Joanna Soltys, Mattie Poelsterl, Omar Sharaf, and Victor Agaba 

3 Prerequisites 

• Comp Sci 111 AND (Comp Sci 211 OR Comp Sci 150) 

• Comfort with programming. 

• Basic discrete math. See Chapter 2 of the textbook. 

4 Learning Objectives 

• Functional correctness: can you apply data structures and algorithms concepts to produce implementations that display correct behavior and produce correct results? 

• Non-functional correctness: can you produce programs that are efficient, robust, and maintainable, and demonstrate good programming habits and hygiene? 

• Theory: can you understand, apply, and contextualize definitions and techniques related to the theoretical aspects of data structures and algorithms: computational complexity, invariants, etc. 

• Evaluation: can you make informed decisions when picking data structures and algorithms and evaluating alternatives? 

• Integration: can you combine multiple data structures and algorithms to decompose and solve complex problems?

5 Communication Channels 

In this class, we will rely on a variety of communication channels, each serving a different purpose. 

5.1 Lectures 

Lectures will be synchronous and in person. We will make a best effort attempt at recording them. The recordings will be available from Canvas under the "Panopto" tab. Please see the recording policy at the end of this document. Recordings are not substitutes for attending lectures, merely supplements. 

We expect you to come to lecture and participate. 

To ensure the health and safety of our community, DO NOT come to lecture if you have reason to believe you may be sick with COVID-19 (or anything else, really). Please watch the recordings instead and contact the instructor. Please see the COVID-19-related policies at the end of this document. 

Questions 

I love questions! Keep ’em coming. During lecture, please ask questions in one of two ways: 

• Raise your hand and ask verbally. 

• If you prefer to ask anonymously and/or textually, reply to the Piazza (see below) post of the current lecture. A member of the course staff will then ask your question in your stead. 

5.2 Piazza

We will use Piazza for discussion, Q&A, and announcements. You can find the class’s Piazza instance here: https://piazza.com/northwestern/fall2023/comp_sci214 

We expect you to subscribe to the 214 Piazza board ASAP and monitor it regularly. 

The entire course staff, as well as many of your fellow students, will be monitoring Piazza. As such, it is the most effective way to get answers quickly; more effective than contacting individual members of the course staff! A few guidelines for using Piazza productively: 

• Look before you post: The main advantage of Piazza is that common questions are already answered, so search for an existing answer before asking a question. Posts are categorized (e.g., "hw1") to help you search. 

• Don’t share your solutions: Everyone in the class can see public posts. So if you post elements of your solution in a public post, you’ve just spoiled everyone’s learning. Not good. If your question involves specifics of your solution, make a private post ("visible to Instructors only") to keep your hard work hidden from prying eyes. 

• Post publicly by default: In all other cases, public posts are preferable: your fellow students can answer (so you get an answer faster!) and other students can benefit from the answer as well. 

• Ask precise questions: Posting a big chunk of code and then saying "help, I don’t get it" is not an effective way to learn. Instead, narrow down your problem to the point where you can ask a specific, precise question, e.g., "on the 3rd line I get this error, what does it mean?" 

For questions that require longer discussions/explanations, please attend office hours (see below) instead. If all else fails or for sensitive matters, send the instructor an email, either for discussion or to schedule an appointment.

5.3 Canvas 

We will use Canvas to distribute materials, assignments, and feedback. All (non-exam) assignments will also be submitted via Canvas. We expect you to check Canvas regularly. Please do not use Canvas messages/comments to communicate with us; they are very easy to miss. Please use Piazza instead. 

5.4 Office Hours 

We will hold a combination of online and in-person office hours, circumstances permitting. We will maintain an up-to-date schedule (with locations) on the "Office Hours" page on Canvas. We will monitor attendance patterns and adjust the schedule to the best of our ability to limit crowding. 

Online office hours will be held using the gather.town platform. The room we will use is at: https://app.gather.town/app/fQLpVpi2VqWouD4N/Comp-Sci-214-Office-Hours 

See the "Office Hours" page on Canvas for the password to the room. 

To ensure we can help students in a first-come first-served order, and to help us group up people with similar questions, we ask that you sign up on this form when you arrive to office hours, or when you have a follow-up question: https://forms.gle/1pC2AXdkLeaDpkw99 

You can look at the state of the queue to give you an idea of how close you are to being next in line: https://tinyurl.com/2f4jar72

Expectations 

Office hours are a great place to get advice and assistance on all aspects of the class (and even CS in general!) In particular, they are not limited to debugging assistance; conceptual questions are welcome too! 

Keep in mind, however, that our course staff is here to help you learn, not to give you solutions or to solve problems for you. An important part of the learning process is learning to figure things out and solve problems on your own; having members of the course staff do that for you would be doing you a disservice! 

This means you should expect our course staff to give you advice and strategies or point you to relevant resources (e.g., documentation, slides, videos), but not to hold your hand (so to speak) while you work through problems. This will be especially true later in the quarter as assignments get more challenging and office hours get busier; to make sure everyone can get help, they won’t have time to do that. 

To get the most out of office hours, come prepared. Please come with specific questions or issues: i.e., not "I don’t know what to do." or "Where do I start?". And be ready to tell us what you have tried to resolve them: e.g., you wrote some test cases to narrow down where a bug could be, you’ve added some printing to understand what your code was doing. This will make it much easier for our course staff to figure out what’s going on, and they’ll be grateful. 

Ultimately, though, none of us are psychic: it’s absolutely possible there may be an issue with your code which we cannot figure out. The person who is in the best position to understand it is the person who wrote the code and has been staring at it for hours: i.e., you. Our job is to give you the tools you need to get to that understanding. 

5.5 Additional Help 

Piazza and office hours should be the first steps for getting help. If you require additional 1:1 help, email the instructor. Do not ask individual members of the course staff for 1:1 appointments directly; to ensure an equitable workload distribution across staff members, we manage such requests centrally.

6 Resources 

Textbook 

For some of the topics we will discuss, we will follow the draft of a textbook I am writing for this class; the tentative schedule later in this document details how lectures and textbook chapters line up. 

For the most part lectures are intended to be standalone, with the textbook as a supplement for students who want to reinforce their learning or go deeper into a topic. Some topics, however, will not be covered directly in lecture; you will therefore need to read the relevant textbook chapter to be able to understand what follows in class. These topics will be announced in advance. 

In addition, if you are looking for a reference, Cormen, Leiserson, Rivest, and Stein’s Introduction to Algorithms is the standard one. It is very comprehensive, but not always easy to approach. Its emphasis is also more theoretical than ours. Nonetheless, it is a useful book to have on hand for computer scientists. 

Supplementary Videos 

A variety of supplementary videos about various 214-related topics are available on Canvas (on the page of that name). These have been produced by (current and former) members of our course staff, and provide additional information and advice that we don’t have time to cover in lectures. We recommend watching them early on; they offer valuable advice that can be quite helpful when working on assignments. 

7 Assessment 

This class uses a form of Specifications Grading (Nilson 2015). In particular, it features mainly qualitative assessment, and does not rely on points, percentages, and weighted averages to determine final grades. 

This is likely quite different from what you have seen in your other classes, and will take some getting used to on your part. This system, however, has a number of benefits for students—see section 7.6. 

7.1 Programming Assignments 

This class features five programming homeworks and a final project that has a programming component, which chiefly contribute to our functional correctness learning objective. All programming assignments and related sub-assignments (see below) must be done individually; see our Academic Integrity policy later. When evaluating your programming submissions, we will give you feedback regarding where they fell short as well as assign the following possible outcomes: 

• Got it: your submission implements the specification correctly and in full. 

• Almost there: your submission implements the specification with minor mistakes or omissions. 

• On the way: your submission has elements of a correct solution, but requires non-trivial further work to be correct and/or complete. 

• Not yet: your submission requires significant further work. 

• Cannot assess: we could not evaluate your submission as is; repairs are required before we can do so. 

Specifics of what is required to achieve each outcome are included in the handout for each assignment; our expectations are stated up front, no surprises.

Resubmissions 

Making mistakes and correcting them is a natural part of the learning process. An outcome of On the way or Not yet is NOT a failure: it just means you still have things to learn from this assignment. Our assignments are intentionally challenging and our grading is intentionally strict in order to maximize your learning; it’s perfectly normal to not get them entirely correct on the first try. 

To give you the opportunity to incorporate our feedback and get credit for your improved learning, we will allow you to resubmit the code portion of each homework one week after the initial deadline.1 To determine final grades, we will use the best of the two outcomes your submissions have achieved. 

Given the scale and importance of the project, we will give you two opportunities to resubmit it instead of just one, and consider the best of the three outcomes. Resubmissions are optional: if you’re happy with your first submission, you can skip the second.

Self-Evaluations 

Each of the five programming homeworks will be followed by a self-evaluation, due one week after the initial deadline. Self-evaluations will consist of a few questions about the code you turned in² .

The purpose of these self-evaluations is to evaluate your submissions on non-functional correctness: desirable aspects of programs that go beyond strictly producing correct answers. Aspects like efficiency, testing, robustness, or factoring. In actual programming (i.e., not in classes), these are just as important (if not more so) than raw functional correctness. This class will give you opportunities to cultivate these skills and habits as you work on assignments. 

Project Design Documents 

Instead of a self-evaluation, your final project will be accompanied by design documents to explain your design and provide rationales for your decisions. More details will be included in the final project handout. The final project and its associated design documents contribute to our evaluation and integration learning objectives. 

7.2 Worksheets 

To evaluate specific aspects of our theory learning objective, we will have two worksheet-style assignments. 

To ensure everyone has a solid theoretical foundation, these worksheets will be done as Canvas quizzes with unlimited retries; we encourage you to re-do them until you fully understand. 

7.3 Exams 

We will have two in-person, on-paper midterm exams which will chiefly assess our theory learning objective. No notes or electronics (laptops, calculators, tablets, phones, smart watches, tamagotchis, etc.) will be allowed during exams. Specifics and logistical details will be announced leading to each exam. 

7.4 Final Grades 

Final letter grades will be determined as follows. 

Base Grade 

Your base grade will be determined based on your performance on homeworks and the final project: 

	A- Great	B Good	C+ Fine	D Poor
Homeworks # Got it  # Almost there (or better) # On the way (or better) # not counted	all 5 — — —	3 1 1 —	2 1 1 1	1 1 1 2
Final project Minimum outcome	Got it	Almost there	On the way	On the way

The criteria in each column are the minimum criteria for that base grade. Note that the counts within a column should all refer to distinct homework assignments; for example, the one Almost there or better homework for a B base grade is in addition to the three Got it ones. To determine your base grade, check your homework and project outcomes against the first column. If at least one criteria in the column does not apply, move on to the next column. Keep moving until you reach a column for which your outcomes apply: this is your base grade. Failing to meet the criteria for a base grade of D will result in automatically failing the class (i.e., an F). 

For example, a student whose homework submissions earned 4 Got it but one On the way and whose project submissions earned an Almost there would earn a base grade of B. 

As another example, a student whose homework submissions earned 2 Got it, 2 On the way, 1 Not yet, and whose project submissions earned an Almost there would earn a base grade of D. 

Warning: these criteria put a lot of importance on the final project, which is intentional. We believe3 that the learning experiences from the project are especially valuable. We therefore consider it very important, and we want you to do so as well.

Modifiers 

This base grade will then get adjusted with modifiers based on your performance in other aspects of the class. Each of these can either increase your grade by some number of partial letter grades, leave your grade unchanged, or decrease your grade by some number of partial letter grades. See the tables below for the possible modifiers in this class.

	+1	0	-1
Worksheets	Both 100%	Both ‌≥80%	Either <80%
Self-evaluations # 5/5s # ≥ 3/5s # ≤2/5s	≥4 — ≤1	— ≥3 ≤2	— ≤2 ≥3

Both worksheets are considered together to determine a single modifier; both must meet the expected level. Similarly, all five self-evaluations are considered together to determine the self-evaluation modifier. 

	+1	0	-1	-2	-3
Exam #1	18-20/20	12-17/20	8-11/20	4-7/20	0-3/20
Exam #2	18-20/20	12-17/20	8-11/20	4-7/20	0-3/20
Exam improvement	#2 mod. ≥ #1 mod. + 2

Each of our two exams will be out of 20 points. Depending on the score you earn on an exam, its modifier will range from +1 (an increase of one partial letter grade) to -3 (a decrease of a full letter grade), with the plurality of scores leading to a neutral modifier (no change). 

In addition to each exam providing its own modifier, if your performance improves significantly between the two exams, you will earn a positive (+1) modifier. Here, "significantly" means the second exam is two modifier steps (or more) above the first one: e.g., going from a -1 to a +1, or from a -3 to a 0. 

	+2	+1	0	-1
Project program	2x Above expectations˚	Above expectations˚˚	Meets  expectations
Project documents		Outstanding	Minor errors or omissions	Major errors or omissions

˚Base grade requires On the way, earned Got it. 

˚˚ Base grade requires On the way, earned Almost there. Or requires Almost there, earned Got it. 

Finally, the final project contributes two modifiers: one for your design documents, and one for exceeding the expected outcome set by your base grade. 

For example, suppose our first hypothetical student from before who earned a base grade of B got everything right on both worksheets (+1), submitted self-evaluations that earned three 5/5s and two 2/5s (0), did poorly on exam 1 (-1) but well on exam 2 (0), and wrote excellent project documents (+1). This would work out to an increase of one partial letter grade, for a final grade of B+.

For another example, suppose our other hypothetical student who earned a base grade of D did poorly on exam 1 (-1) and great on exam 2 (+1 on its own, and +1 for improvement), earned an Almost there on the project (above expectations for a D base grade, so +1) while having +1 for the worksheets, +1 for the project design documents, and 0 for self-evaluations, this would result in a total of +4. Starting from a base grade of D, our hypothetical student would earn a final grade of B-. 

For reference, the possible letter grades at Northwestern are: F, D, C-, C, C+, B-, B, B+, A-, and A. Going up one partial letter grade means going forward one step in this sequence, and going down one letter grade means going backwards one step. 

The letter grade sequence "saturates" on either end: applying +4 to a base of B results in an A (there is no A+), and applying -2 to a base of D results in an F (there are no D-, F+, or F-). 

7.5 Late Policy 

Unless otherwise indicated assignments are due by 11:59pm on their due date. 

To accommodate everyday slippage and minor life hiccups, each student starts the class with three late tokens. Each of these late tokens can be exchanged for a two-day no questions asked extension to any (nonexam) deadline, covering all assignments, resubmissions, etc. due that day. Only one token can be used per deadline; i.e., two days is the maximum extension possible. Late tokens will be used automatically when you submit late; you don’t need to reach out to us and ask for permission. 

In the event a student runs out of tokens, I’ll be in touch. Being occasionally late is perfectly fine. Being consistently late, on the other hand, may be a sign of deeper underlying issues: let’s talk so we can figure out the best way to help you. 

To make deadlines and accommodations equitable for all students, we will not grant further ad-hoc extensions or accomodations, barring extreme circumstances. If you do end up in a situation that would warrant additional flexibility, you must contact your dean of students6 and have them contact me. They will help you coordinate extensions and accomodations across all your classes, which will ensure you get the support you need across the board, not just in this class. 

If you require accommodations for medical reasons, you should follow the process described here: https://tinyurl.com/d84bvvzj 

7.6 Why this system? 

This assessment system carries many benefits to you as a student. 

• Learning and grades align. Learning is our true goal here; grades are (at best) a distraction. But given the impact grades can (unfortunately) have beyond the classroom, ignoring them altogether may not be an option for everyone. As a compromise, this system ties grades directly to concrete achievements and milestones in your learning, rather than on an amorphous pool of fungible points of assorted provenances. 

• You are in control. You decide what grade you want to aim for, and the expectations you’ll need to meet for that goal are stated up front and transparently. Grades are determined solely by your achievements, not by those of your classmates; no percentage cutoffs, curves, or other such nonsense. No more "hoping" for a grade, and no more surprises when letter grades come: you can always tell exactly how things are going, and know what you need to do if you want to change it. 

• High standards, low stakes. You’re all super smart. Yes, that means you too. You all have the potential to learn a lot in this class and grow as computer scientists. To inspire you to learn as much as you can, we will hold you to high standards; one doesn’t learn much by consistently producing mediocre work. At the same time, we recognize that high standards can carry a lot of pressure; which is why we’re also including mechanisms to lower stakes whenever possible (see next item). 

• There is room for mistakes. Making mistakes is part of the learning process, and you should get credit for learning from them. Resubmissions give you an opportunity to do so. And for places where resubmissions don’t make sense (e.g., exams, self-evaluations), some slack is built-in to the grading standards to allow for small slips, and the consequences of even larger slips are bounded. 

• Flexibility built-in. Life happens; we recognize that. But not all students feel comfortable asking for flexibility when necessary, either as a matter of personality or of upbringing. For this reason, "hiding" flexibility behind explicit requests is not equitable. Instead, this system comes with built-in flexibility in a number of places—resubmissions, late tokens, many possible modifiers, etc.—all of them with no questions asked and no need to ask for it. That way everyone benefits, regardless of comfort level. 

Caveats 

That being said, this is a large class–which introduces issues of scale and equity—and we’re limited by the length of the term—which introduces issues of scheduling. This means we’ve unfortunately had to make some compromises when designing our assessment strategy. 

In an ideal world, we would assess your learning directly using mechanisms like oral exams and code walkthroughs, with infinite retries and no hard deadlines. But we are not in an ideal world, so we’re forced to assess your learning indirectly through your work using automated testing, written exams, and other mechanisms geared towards efficiency. These mechanisms, while sadly necessary in our context, may not reflect your learning 100% accurately. 

Therefore, it is your responsibility to make sure the work you submit is as close a reflection of your actual learning as possible, so we can get the best picture we can of where you’re at as a student. And in turn, so you can get as much recognition as possible for your learning. Concretely, this means: 

• For programming assignments, being thorough in your testing to avoid silly mistakes, and taking full advantage of our feedback when working on resubmissions. 

• For "one-shot" assessments like exams or self-evaluations, be very careful to read instructions completely and thoroughly, and ask for clarifications if need be. We can’t assess your learning if you answer the wrong question.

8 Software 

Code examples and programming assignments will use the DSSL2 (Data Structures Student Language version 2) language. It runs on top of the Racket environment. You will need to install version 8.10 from: download.racket-lang.org 

If you have an old version from a previous course, some code will not work. 

Then, to install DSSL2 proper, from DrRacket open the File menu and select Install Package..., then type dssl2 as the source. Then click Install. When it’s done, the Install button will change to Update, indicating that the package is installed. 

To familiarize yourself with the language, you should consult the DSSL2 reference: https://docs.racket-lang.org/dssl2/ 

8.1 Why DSSL2? 

We designed DSSL2 as a language specifically for learning data structures. It features everything we need to build and reason about data structures and related concepts, while at the same time avoiding distractions and pitfalls from other languages that, while very useful in practice, are not relevant to our goals. 

DSSL2 is a close cousin of Python, so if you know Python, you will be able to pick up DSSL2 easily and vice versa. We specifically do not use Python (or other production-ready languages) because not only do they have the aforementioned distractions, they also include a whole cornucopia of useful data structures already! This makes perfect sense for large-scale programming in practice, but is not appropriate for learning; we want to be building those ourselves! 

Note also that this class is not a programmiing class. It is a computer science class, where learning a specific language is not our learning objective. The concepts and programs you see in this class are fundamental and universal: they apply to whatever language you choose, now or later in your career. 

8.2 Development Environment 

The DrRacket IDE (which comes with Racket, and which you may have used in 111) has the most complete DSSL2 integration of any environment; that’s the one I encourage you to use. If you’re already familiar with other environments, varying levels of DSSL2 support are also available for: 

• VSCode: your colleague Joshua Irvin wrote a DSSL2 plugin for VSCode.7 I have not tried it, though, so caveat emptor. 

• Vim: Vim users are welcome to try your colleague Marko Vejnovich’s DSSL2 plugin.8 I have not used it myself either. 

• Emacs: Emacs’s Python mode offers adequate, but far from ideal DSSL2 support. 

You’re welcome to use these alternative environments if you wish, but neither I nor the course staff will be able to offer you support. You’ll be on your own. 

8.3 Hardware 

By default, we assume that you will be using your personal computer to work on assignments. If need be, you should also have physical access to the Wilkinson Lab, as well as login access to the computers there. 

If you cannot access these computers, please reset your password at: https://selfserv.eecs.northwestern.edu/temp_password/ 

If that does not work, please contact [email protected]. Similarly, contact root if you do not have physical access to the labs. 7https://github.com/Gamefreak130/dssl2_vscode_extension 8https://github.com/markovejnovic/vim-dssl2/

9 Academic Integrity 

The trust employers, colleagues, and the public put in a Northwestern degree is built on the premise that our grades are an accurate reflection of student learning and effort. Any attempt at subverting the relationship between learning and grades is a threat to this hard-earned trust and, left unaddressed, hurts all Northwestern students. As such, we take any attempts at violating academic integrity very seriously. 

In this class, we expect all students to know, understand, and abide by the McCormick Academic Integrity policy,9 as well as the course-specific policies below. Failure to abide by these policies is a serious offense which carries serious consequences.

Fundamentally, anything you turn in for this class must be your own individual work, and be the product of your own thinking and reflection. There are two main ways in which your work may fall short of this expectation: accessing unauthorized sources and engaging in excessive collaboration. 

9.1 Unauthorized Sources 

Sources which you are allowed to use while working on assignments: 

• Materials posted to this course’s Canvas or Piazza this quarter. 

• Interactions with members of the course staff, be they in office hours, on Piazza, etc. 

• Feedback you receive from us on your work, for example in homework grading reports. 

• That’s it. 

In contrast, sources which you are not allowed to use or even consult while taking this class: 

• Submissions or work in progress from other students, past or present, in full or in part. This includes test cases; those are part of submissions. 

• Solutions or solution fragments you may find online or elsewhere. 

• Course materials from previous quarters, regardless of how you obtained them. 

• Outside tutors or "work-for-hire" services. 

• Code generation tools (AI-based or otherwise), such as Github CoPilot, ChatGPT, etc. 

• This list is not exhaustive; ask the instructor before using any source not explicitly listed above. 

Using, or even having access to, unauthorized sources constitutes a violation of this class’s academic integrity policy, regardless of the extent to which the source was actually used, or even if it was used at all. 

Similarly sharing, posting, or otherwise making available your own solutions (in full or in part) or any course materials (homeworks, exams, solutions, test cases, etc.) is also a violation of our academic integrity policy. This extends even after the quarter ends; course material remains private information which you may not share or reproduce. 

A note for students retaking the class: resubmitting or otherwise reusing your work from earlier attempts at the class is also considered plagiarism. You should delete any old work you may have, and you must do all work from scratch; otherwise you will not learn as much as you should.

9.2 Excessive Collaboration 

Collaboration is a good thing, and something you’ll be expected to do in the workplace. However, because this is an introductory class, we also need to ensure that every student has a solid foundation, which will allow them to be effective and productive collaborators throughout their career. For this reason, students must tread very carefully when collaborating in this class, to ensure they do not (accidentally or willfully) cross the line into excessive collaboration, with all the negative effects on learning this implies. 

When working with current or former students in this class, you are limited to arm’s-length collaboration. When collaborating at arm’s length: 

• You may not read, write, look at, record, or in any way transcribe solution elements from someone else. In turn, your collaborator may not read, dictate, or otherwise share elements from their own (or anyone else’s) solution with you, nor can they directly read, write, or look at your own solution either. 

• You may not have your own solution on your screen or otherwise in your field of vision while collaborating, and neither must your collaborator. 

• You must cite any collaborators whose ideas affected your work, and explain the effect the collaboration had on your solution. Bear in mind, however, that citing a collaboration does not absolve you from the aforementioned limits on arm’s-length collaboration. 

To avoid getting close to the line, we strongly recommend you keep any discussions with current or former students either at a conceptual level, or about non-graded aspects of the class (e.g., examples from course materials). 

You are of course always welcome to seek help from any member of the course staff on assignments; the above limitations do not apply to such interactions. 

As always when in doubt, ask the instructor before doing something you’re not sure about. 

9.3 Suspected Violations 

Any suspected violation will be reported to the McCormick Office of the Dean. The Dean will conduct an investigation to determine whether a violation did take place or not, which will involve meeting with you. Should you find yourself in this situation, be forthright and honest; lying or withholding information from the Dean is a further offense, and can make a bad situation much worse. 

10 Welcoming Environment 

I consider this classroom to be a place where you will be treated with respect. I welcome all students, and expect all of you to do the same. Together, we can create an environment where everyone feels welcome and can engage fully in our community. 

Each student has something of value to contribute, especially in engineering disciplines where empathy, communication, and teamwork elevate our contributions to society; and lack thereof can lead to disaster. Individual differences can deepen our understanding of one another, the world around us, and our lifelong role as engineers. 

(Credit: Adapted from statements by the ASEE and Prof. Emma DeCosta)

11 Miscellaneous Statements 

The registrar has a lot of things they want me to tell you. 

11.1 Accessibility Statement 

Northwestern University is committed to providing the most accessible learning environment as possible for students with disabilities. Should you anticipate or experience disability-related barriers in the academic setting, please contact AccessibleNU to move forward with the university’s established accommodation process (e: [email protected]; p: 847-467-5530). If you already have established accommodations with AccessibleNU, please let me know as soon as possible, preferably within the first two weeks of the term, so we can work together to implement your disability accommodations. Disability information, including academic accommodations, is confidential under the Family Educational Rights and Privacy Act. 

12 Tentative Schedule 

Dates and content are subject to change. Any changes will be announced on Piazza. Date Homeworks Self-Evals and Lecture Topic Resubmissions (and Reading) Tue 9/19 1 out Intro, DSSL2 Basics (Chapter 2) Thu 9/21 DSSL2 Q&A, Linked Lists Tue 9/26 1 due, 2 out Abstract Data Types, Stacks, Queues Thu 9/28 worksheets out Asymptotic Complexity Fri 9/29 1 out Tue 10/3 2 due 1 due Sorting Thu 10/5 Dictionary ADT (Chapters 6 and 7) Fri 10/6 2 out Tue 10/10 worksheets due, 3 out 2 due Hash Tables (Chapters 8 and 9) Thu 10/12 Graph ADTs and Representations (Chapters 10, 11, and 12) Tue 10/17 First Midterm Exam Thu 10/19 4 out Graph Search (Chapter 13) Tue 10/24 3 due Single-Source Shortest Path (Chapter 14) Thu 10/26 5 out Priority Queue ADT (Chapter 15) Fri 10/27 3 out Tue 10/31 4 due 3 due Data Design Thu 11/2 project out Data Design, continued Fri 11/3 4 out Tue 11/7 5 due 4 due Minimum Spanning Trees, Disjoint Sets ADT Thu 11/9 Self-Balancing Trees Fri 11/10 5 out Tue 11/14 1st project deadline 5 due Amortized Analysis Thu 11/16 Relational Model Tue 11/21 2nd project deadline Probabilistic Data Structures Tue 11/23 Thanksgiving: no class Tue 11/28 Persistent Data Structures Thu 11/30 Second Midterm Exam Tue 12/5 3rd project deadline 14