Quick Links: Grading<\/a> | Daily Schedule<\/a> | Brightspace<\/a> | <\/strong>Class Code (GitHub)<\/strong><\/a><\/p>\n\n\n\n Prof. Daniel R. Schlegel, 464 Shineman Center, daniel.schlegel@oswego.edu<\/a> A developing computer scientist must understand and explain how their proposed data structures and algorithmic solutions compare to other solutions in terms of complexity, run time, and resource requirements. This course introduces students to traditional techniques used to describe such solutions. In addition, we will look at classic data structures and their applications in order to expand the depth and breadth of a student\u2019s knowledge.<\/p>\n\n\n\n This course is intended to challenge the student to design and implement software based on specifications prepared by the instructor. Throughout the semester, each student will need to identify appropriate design elements and justify their selections.<\/p>\n\n\n\n Recommended:<\/span> Koffman, E.B. and Wolfgang, P.A.T., Data Structures: Abstraction and Design Using Java, 3e<\/em>. Wiley, 2015.<\/p>\n\n\n\n Introduction to Computer Science Using Java<\/a> As per college policy, attendance in all sessions is obligatory. If you cannot attend a class meeting due to religious, athletic, health related circumstance, or circumstance of particular hardship, please notify me in advance via email. Please be ready to present proof, if necessary. <\/p>\n\n\n\n We meet in person three times a week. There will be no recording, and I won\u2019t work through the content with you in a one-on-one fashion during office hours or an appointment \u2013 basically, if you miss class then you missed out on what you signed up for when registering for the class and you will have to work through the content independently. Of course, I will answer questions about the content, if you have any, and there are exceptions for excused absences due to illness etc.<\/p>\n\n\n\n \u201cGo Remote\u201d Days Due to Weather<\/span>: In short, we value our snow days and will do what is reasonable to make sure that no commuter is in danger, while also maintaining academic integrity. In long, the concept of a “Go Remote” day due to weather is crap. We deserve our snow days. Yes, snow days are a serious inconvenience and create a content crunch, especially if classes are cancelled indiscriminately. But, snow days are also a weather-gifted day of respite and nearly everyone appreciates an occasional day of rest. We may choose to skip certain topics during the semester so that we can afford to cancel class on a “Go Remote” day. If it turns out that many classes are cancelled due to weather, then we will have to assign independent reading \/ activities to cover some of the content.<\/p>\n\n\n\n A positive learning environment relies upon creating an atmosphere where all students feel welcome. Discussion is meant to allow us to hear a variety of viewpoints. This can only happen if we respect each other and our differences. Hostility and disrespectful behavior is not acceptable.<\/p>\n\n\n\n Cell phones and headphones should not be out or used during lecture, and laptops should only be used for taking notes. If use of any electronics becomes distracting to other students I reserve the right to discontinue the allowance of their use.<\/p>\n\n\n\n All assignments will be completed alone, but working together without writing or sharing code<\/strong> to come up with general solutions is encouraged. The assignments are difficult, and I recommend starting work on them early, avoiding any tendency toward procrastination<\/a>. You should plan on spending at least<\/em> 10 hours per week on course work outside of class.<\/p>\n\n\n\n No AI or large language models should be used during CSC241. These tools are great and you will be using them in later courses, but foundational skills must be learned through reading, experimentation, trial and error, struggle, and hard work. The skills you learn in this course will help you decide if the answers given by AI models are really helpful or not in your later work, so don’t deprive yourself of that! <\/p>\n\n\n\n Assignments will be submitted via Brightspace and graded according to the grading criteria<\/a>. Code which does not compile or immediately crashes will receive no credit.<\/strong> There may be in-class presentations and\/or live demos of your work at various points in the semester.<\/p>\n\n\n\n Assignments may be submitted up to five calendar days late with a 5% per day penalty. After five days no credit will be assigned. Outside of this late policy, no late assignments will be accepted. Assignments submitted late will be graded later than those submitted on time.<\/p>\n\n\n\n The total points received on assignments will then summed and divided by the points possible and scaled as appropriate according to the percentages given below.<\/p>\n\n\n\n \nInstructor:<\/span><\/h4>\n\n\n\n
Office\/Lab hours: Monday\/Wednesday 2-4PM. Email any time with questions!
Section 810: MWF 9:10-10:05, Shineman 444<\/p>\n\n\n\nCourse Description:<\/span><\/h4>\n\n\n\n
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Textbooks:<\/span><\/h4>\n\n\n\n
Useful Resources:<\/span><\/h4>\n\n\n\n
Data Structures\u00a0@ Wikibooks<\/a>
Data Structure Visualizations<\/a>
Java Tutorials\u00a0@ Oracle
<\/a>JDK 21 Documentation @ Oracle<\/a>
Java on LinkedIn Learning<\/a><\/p>\n\n\n\nAttendance and Modality:<\/span><\/h4>\n\n\n\n
Classroom Etiquette:<\/span><\/h4>\n\n\n\n
Asignments:<\/span><\/h4>\n\n\n\n
AI Policy:<\/span><\/h4>\n\n\n\n
Grading:<\/span><\/h4>\n\n\n\n