Syllabus
Computer Science 202 Spring 2024
Course Instructor
- Instructor Name: Dr. Gregory M. Kapfhammer
- Office Location: Alden Hall 108
Please visit the instructor’s web site for more information!
Instructor Appointments
- Monday: 11:00 AM – 12:00 noon
- Tuesday: 1:30 PM – 2:30 PM
- Wednesday: 10:00 AM – 12:00 noon
- Wednesday: 2:00 PM – 3:00 PM
- Friday: 2:30 PM – 3:30 PM
All instructor appointments are 15-minute time slots and take place in Alden Hall, Room 108.
Course Description
A study of fundamental methods for designing and implementing algorithms and analyzing their efficiency. While developing expertise in select models of computation and the key mathematical and experimental approaches to studying algorithm efficiency, students investigate different types of algorithms through hands-on activities that often require teamwork. Students also learn how to determine whether a problem can be efficiently solved by an algorithm that is implemented as a computer program. During a weekly laboratory session students use state-of-the-art technology to complete projects, reporting on their results through both written documents and oral presentations. Students are invited to use their own departmentally approved laptop in this course; a limited number of laptops are available for use during class and lab sessions.
- Prerequisite: CMPSC 101
- Distribution Requirements: QR, SP
Learning Objectives
Allegheny College’s educational program is designed so that its graduates are able to:
AC-1: Think critically and creatively.
AC-2: Communicate clearly and persuasively as speakers and writers.
AC-3: Invoke multiple ways of understanding to organize and evaluate evidence, and to interpret and make sense of their experiences and the experiences of others.
AC-4: Apply their knowledge and learning to engage in informed debate, and to analyze and solve problems.
Computer Science 202 at Allegheny College is a core course in the Computer Science major. Graduates with the Computer Science major — who all take the Computer Science 202 course — must demonstrate their attainment of these learning objectives:
CS-1: Demonstrate and be able to communicate the knowledge of data types, algorithms, and mathematical principles behind discrete objects.
CS-2: Use scientific and theoretical methods to design, implement, evaluate, deploy, improve, maintain, and document software and hardware systems.
CS-3: Apply and articulate key concepts from a specialization area where the interconnection between software and hardware is important and evident.
CS-4: Able to communicate technical details of the produced software and hardware artifacts both in writing and orally.
All five of the Computer Science major’s learning objectives support the QR and SP distribution requirements and the College’s learning objectives.
The specific learning objectives for Computer Science 202 are as follows:
CS-202-1: Correctly implement both well-established and custom data structures using a programming language so as to solve a problem with a computer program.
- Enables the attainment of CS-1.
CS-202-2: Perform an asymptotic analysis of an algorithm to arrive at its correct worst-case time complexity class.
- Enables the attainment of CS-2.
- Enables the attainment of CS-4.
CS-202-3: Conduct experiments that measure the efficiency of different combinations of programming languages, data structures, and algorithms.
- Enables the attainment of CS-3.
CS-202-4: Use both theoretical and experimental results to pick the data structure(s) and algorithm(s) that balance the trade-offs associated with correctly and efficiently solving a problem with a computer program.
- Enables the attainment of CS-2.
CS-202-5: Effectively apply algorithmic problem solving techniques like searching, sorting, and memoization to correctly and efficiently solve a problem through the use of a computer program.
- Enables the attainment of CS-1.
The learning objectives for Computer Science 202 enable the attainment of the CS program learning objectives that in turn support the attainment of the College’s learning objectives.
Course Textbooks
Online textbook called A First Course on Data Structures in Python The ds2package in the donsheehy/datastructures GitHub repository
Extensive details about the content that the course covers in the textbook are available in the course schedule and the course slides.
Course Policies
Assessment
The grade that a student receives in this class will be based on the following categories. All of these percentages are approximate and, if the need to do so presents itself, the course instructor may, for instance, change the assigned percentages during the academic semester.
| Category | Percentage |
|---|---|
| Class Participation | 10% |
| Midterm Examination | 15% |
| Final Examination | 15% |
| Algorithm All-Hands | 10% |
| Algorithm Engineering | 50% |
These assessment categories have the following definitions:
Class Participation: Students are expected to regularly attend and actively participate in all of the class and laboratory sessions, as outlined on the course schedule. After either an unexcused absence or a late attendance to either a class or a laboratory session, a student’s weekly class participation grade will be reduced. Students who need to miss class or attend class late for an excused reason should communicate their situation to the course instructor in a timely fashion. A student’s weekly class participation grade will be reduced if they are frequently observed, during either class or laboratory sessions, undertaking non-course-related activities like viewing email, social media, or other content not about algorithm analysis.
Midterm Examination: The midterm examination is an online, executable, and cumulative assessment covering all prior material from the course sessions, as outlined on the course schedule. Unless prior arrangements are made with the instructor, all students should use their computer to take these tests on the scheduled date and to complete it in the stated location while taking no more than the required amount of time. The midterm is an executable examination that students will complete through the use of GitHub, VS Code, and the Python programming tools installed on their laptops. Following the completion of the midterm examination during a laboratory session, students will, during algorithm all-hands sessions, collaboratively complete and present an analytical and empirical evaluation of the performance of one component of their examination. Students may use external sources, including artificial intelligence coding assistants, during the completion of the midterm examination provided that they cite these sources and explain how they used them to complete the examination.
Final Examination: The final examination is an online, executable, cumulative assessment covering all of the material during all of the course sessions, as outlined on the course schedule. Unless prior arrangements are made with the instructor, all students should use their computer to take the final examination on the scheduled date and to complete it in the stated location while taking no more than the required amount of time. The cumulative final is an executable examination that a student will complete through the use of GitHub, VS Code, and the Python programming tools installed on their laptops. Students may use external sources, including artificial intelligence coding assistants, during the completion of the final examination provided that they cite these sources and explain how they used them to complete the examination.
Algorithm All-Hands: These projects enable students to explore both the scientific and engineering aspects of algorithm analysis, as outlined in the course schedule. During the completion of a scientific study in the field of algorithm analysis, students will complete and present a team-based project in which they propose and then answer a research question about the performance of a Python program. When completing an engineering effort in the field of algorithm analysis, students will build and present a team-based project in which they design, implement, document, release, and maintain software tools that support the rigorous evaluation of the performance (e.g., time or space overhead) of a Python program. Students may use external sources, including artificial intelligence coding assistants, during the completion of an algorithm all-hands project provided that they cite these sources and explain how they used them to complete their part(s) of an algorithm all-hands project.
Algorithm Engineering Projects: These projects enable students to explore both the scientific and engineering aspects of algorithm analysis, as outlined in the course schedule. During the completion of this two-week project, students will design and implement a software system suitable for conducting experiments and then use it answer research questions that connect to previously discussed scientific content, as outlined in the course schedule. After implementing the benchmarking system, students will propose and formally state research questions and then discuss them with the course instructor during a laboratory session. Finally, students will conduct experiments to answer these research questions, collect data, analyze the results, and write a report that explains the results in the context of the implemented system. Students may use external sources, including artificial intelligence coding assistants, during the completion of an algorithm all-hands project provided that they cite these sources and explain how they used them to complete the various components of an algorithm engineering project.
Assessment Policies
Unless exempted by the instructor, students must abide by the following assessment policies:
Assignment Submission
All assignments will have a stated due date shared through GitHub, GitHub Classroom, and/or the Algorithmology Discord. No credit will be awarded for any course work that you submit to the incorrect GitHub repository or web site. Unless special advance arrangements are made with the instructor to address extenuating circumstances, no work will be accepted after the deadline.
Assignment Evaluation
Using a report that the instructor shares with you through your GitHub repositories devoted to work in the field of algorithm analysis, you will privately receive a grade for and feedback on your course projects. Your grade will be a function of whether or not you (a) completed work that fulfills the project’s specification and (b) submitted it by the deadline to the stated platform.
Algorithmology Tokens
Students may “spend” up to five algorithmology “tokens” that they may use to either (i) secure 48 hours of additional time to complete an assigned task or (ii) request a reassessment of an already graded project after revising it according to feedback from the instructor. For instance, a student may choose to spend a token by asking the instructor to reevaluate one of the algorithm engineering projects. Outside of the use of these five tokens, the instructor will not consider any additional requests for deadline extensions or assignment reassessment.
Course Attendance
It is mandatory for all students to attend the course sessions. If, due to extenuating circumstances, you will not be able to attend a session, then, whenever possible, please communicate with the instructor at least one week in advance to describe your situation. Students who have any signs of illness should not attend any in-person course sessions.
Class Preparation
In order to minimize confusion and maximize learning, students must invest time to prepare for the class sessions that focus on professional development, technical development, and project development. Although the course instructor and the student technical leaders will always be available to serve as guide for individual students, teams of students, and the entire class, it is expected that students will volunteer to lead and actively contribute to all class sessions. Only those students who have prepared for class by reading and running the assigned material will be able to effectively participate in these class discussions. To help students remain organized and to effectively prepare for classes, the instructor will maintain a list of course slides and a course schedule with reading assignments, programming suggestions, and other important information about the course (e.g., references to the algorithm engineering projects).
Seeking Assistance
Students who are struggling to understand the knowledge and skills developed in this course are encouraged to seek assistance from the course instructor and/or the student technical leaders. Students should, within the bounds of the Honor Code, ask and answer questions on the Algorithmology Discord Server; please request assistance from the instructor and student technical leaders first through public Discord channels before sending an email or a direct message. Students who need more assistance are invited to schedule a meeting through the instructor’s appointment scheduler and come to the meeting with details about their question. Students can see the office hour schedule for student technical leaders by viewing the list of student technical leaders and by monitoring announcements in the Allegheny College Computer Science Discord Server.
Using GitHub and Discord
This course will primarily use GitHub and Discord for all course communication. We will use GitHub for the sharing of both source code and documentation for course projects and for reporting issues in those materials. We will use two distinct Discord servers for all course discussions. The Algorithmology Discord Server provides a way for members of the developer development community to use text and video to chat with each other and will be the main forum for discussing the professional and technical content in the field of algorithm analysis. The Allegheny College Computer Science Discord Server will be the main forum for Department of Computer Science announcements. Finally, any content that a student wants the instructor to assess (e.g., the work for an algorithm engineering project) must be in a GitHub repository.
Using Email
Although we will primarily use the Algorithmology Discord Server for class communication, the course instructor will sometimes use email to send announcements about important matters such as changes in the schedule. It is your responsibility to check your email at least once a day and to ensure that you can reliably send and receive emails. This class policy is based on the statement about the use of email that appears in The Compass, the College’s student handbook; please see the course instructor if you do not have this handbook.
Honor Code
The Allegheny College Academic bulletin describes The Academic Honor Program that governs the entire academic program at Allegheny College. The Honor Program applies to all work that is submitted for academic credit or to meet non-credit requirements for graduation at Allegheny College. This includes all work assigned for this class (e.g., executable examinations and course assignments). All students who have enrolled in the College will work under the Honor Program. Each student who matriculates at the College acknowledges this Honor Code pledge:
I hereby recognize and pledge to fulfill my responsibilities, as defined in the Honor Code, and to maintain the integrity of both myself and the College community as a whole.
Effective Algorithm Engineering
Students who create the source code and documentation for their algorithm engineering and algorithm all-hands projects should ensure the implementation of a high-quality final product. While students are permitted to use a wide variety of algorithm engineering tools, such as integrated development environments, testing frameworks, automated debuggers, and code generators (e.g., systems that leverage large language models like GitHub Copilot) and documentation sites such as StackOverflow, they must take responsibility for all of the source code and documentation that they submit for this course, including artifacts that are automatically generated by a software tool.
This means that every student must work as an effective algorithm engineer by documenting the sources for their work and verifying the correctness, maintainability, and long-term reliability of all source code and documentation that they submit. As such, students who use software tools to create content are responsible for citing their sources and demonstrating their understanding of it as a part of any follow-on assessment. Moreover, all students in the class are responsible for all of the source code and documentation submitted to the GitHub repository that hosts the course projects, including any tool-generated software artifacts. This means that every student should be able to answer questions, during either an in-person or online discussion, about any algorithm engineering content, including that which was automatically generated by a software tool.
Students who are effective algorithm engineers also pledge to abide by the ACM Code of Ethics and Professional Conduct. Unless the students in this course furnish a different governing contract, they also pledge to follow the principles espoused by exemplary technical organizations, such as Oxide Computer and its public statement of mission and principles.
Disability Services
Students with disabilities who believe they may need accommodations in this class are encouraged to contact Student Accessibility and Support Services (SASS) at 814-332-2898 or studentaccessibility@allegheny.edu. SASS is located in the Center for Student Success in Pelletier Library. Please contact SASS as soon as possible to ensure that approved accommodations are implemented in a timely fashion.
Welcome Message
In reference to software, Frederick P. Brooks, Jr. wrote in chapter one of The Mythical Man Month that “the magic of myth and legend has come true in our time.” Software is a pervasive aspect of our society that changes how we think and act. High quality and efficient software also has the potential to positively influence the lives of many people. Let’s embark on this journey of discovery and innovation, harnessing the power of algorithms to efficiently solve complex problems with software. At the start of this class, I invite you to join together in an adventure in algorithm analysis!