CSSE-230: Datastructures and Algorithm Analysis: Syllabus

Rose-Hulman Institute of Technology
Computer Science Department
Winter 2023/24

Instructor

Prerequisites

Catalog Course Description

Learning Outcomes

1. Describe classical data structures (list, stack, queue, tree, priority queue, hash table, graph, set, dictionary) and explain issues involved in implementation choices for each.
2. Explain classical sorting, graph and tree-balancing algorithms.
3. Develop empirical and mathematical analyses of the asymptotic worst, best and average case run times of algorithms appropriate for this course.
4. Justify the choice of an algorithm based on the analysis of several algorithms appropriate for a problem.
5. Design and implement object-oriented programs competently and independently.
6. Develop and apply unit test cases.
7. Implement various data structures, and apply them to medium-sized programming exercises.

Book

Here is a link to the electronic version

Grading

Item	Weight
Written homework. About seven assignments on various topics covered in class.	15%
Exams. Two in-term and a final.	55%
Programming assignments. About seven programming assignments of varying length, including a team project	30%

Please submit your work to appropriate drop-boxes on Moodle. We use the Moodle gradebook to record scores on assignments.

Policies

• Learning is not a spectator sport! We will have many active learning assignments in class and I will ask for volunteers (or may volunteer students) to answer questions I pose in class.
• Play nice with others policy. We try to learn together and from each other. As such, let us all contribute towards creating a welcoming learning environment. Please treat everyone invilved in this course, fellow students, lab assistants and instructor with utmost respect.
• Students who do not engage with the class or the materials may be asked to drop the course. Alternatively, their course grade may be lowered. Students who actively participate in class will receive a higher course grade, if possible.
• If you feel unwell, for whatever reason, please be a hero and DO NOT come to class. Just send me an email before class and I will turn on Teams. I also have sort videos on the various subjects that you can find on the dialy outlines.
• Submission policy. This is a fast paced course in which materials build on each other. A great way to learn about some of the concepts we discuss in class is by implementing them. It is pertinent that you do not get behind.
• You must have a passing average on the exams in order to pass the course.
• Poor performance on the exams may lower your course grade beyond what results from the calculated score. Excellent performance on the exams may increase your course grade beyond what results from the calculated score.
• You are responsible for all announcements made in class, posted on this web-site and sent by e-mail. If you cannot make it to class, please ask one of your classmates about any announcements that may have been made.
• Academic integrity policy. Basically, all the work you turn in must be your own. Please familiarize yourself with the departmental Academic Integrity Policy. It is in effect for this course. Academic misconduct on an exam will result in an F in the course and an entry in your permanent student file which the Dean of Students keeps. Academic misconduct on any other assignment will result in at least negative points for the assignment and possibly an F in the course as well as an entry in your permanent student file which the Dean of Students keeps. This policy applies to all parties involved in the academic misconduct. I will use plagiarism detection tools. And yes, asking your favorite LLM based-tool to do your homework for you is plagiarism too.
• While we hope you feel comfortable speaking with any faculty in the department, if you have a concern and are not sure where to go, the following CSSE faculty have volunteered to be ombudsmen for the department: Sid Stamm (F216), Amanda Stouder (F222), Kim Tracy (D216) and Robert Williamson (F205).