Intro to Computer Science :: CS 111 :: Winter 2020

calendar :: lectures :: labs

Contact Information

Instructor: Aaron Bauer
Email: awb
Office: CMC 135

Course Information

Course Classroom: Anderson 121

Course Time: 2a (MW 9:50am – 11:00am, F 9:40am – 10:40am)

Visiting Hours:
Mondays 2:00pm – 4:00 pm in CMC 135
Tuesdays 8:00pm – 9:00pm in CMC 304
Thursdays 7:00pm – 8:00pm in CMC 304
(please come find me in my office or send me an email if you want meet another time!)

Prefect: Sam Chen, chens5

Course Staff: Haruki Yoshida, yoshidah and Jeffrey Boitnott, boitnottj
Lab assistant hours for CMC 102

Course Text: Allen B. Downey, Think Python, 2nd edition, available here and in pdf form

The readings in this course are intended to provide an alternative explanation or perspective on the material we cover in class. I recommend going over them (and the associated practice exercises) after the class in which they are assigned as a way of solidifying the concepts.

Announcements will be made via Moodle, so watch for those emails.

Programming Environment: We will be using the Python 3 programming language. The computer science labs (CMC 102, 304, and 306) and the Weitz lab (138) have Python installed on MacOS. You are welcome to use whatever text editor you like. A text editor is an application that's used for writing code; sort of like Microsoft Word, but for code instead of essays. You'll see options like Atom and Visual Studio Code included in the lab computers. The differences between these are not very significant for our purposes; I only recommend using something that is already available on the lab computers. I will normally use Visual Studio Code in class.

Assignment submissions: You will submit assignments by uploading them to Moodle, on the appropriate assignment page.

Final project description

Practice Problems

Textbook practice problems

Course Links

Course Description

From the catalog:
This course will introduce you to computer programming and the design of algorithms. By writing programs to solve problems in areas such as image processing, text processing, and simple games, you will learn about recursive and iterative algorithms, complexity analysis, graphics, data representation, software engineering, and object-oriented design.
This course will focus on a broad introduction to computational concepts. We will use Python 3 as our programming language. No previous programming experience is necessary. By the end of this course, you should be able to:

Course Policies

Grading breakdown:

Percentage Letter
≥ 93 A
≥ 90 ≥ A-
≥ 87 ≥ B+
≥ 83 ≥ B
≥ 80 ≥ B-
≥ 77 ≥ C+
≥ 73 ≥ C
≥ 70 ≥ C-
≥ 67 ≥ D+
≥ 60 ≥ D
< 60 F
Course grade derivation: Course grades are computed by the sum of the points earned divided by the maximum total points. Course grade percentages are converted to letters as shown in the accompanying table. The minimum percentage required to achieve a given letter grade may be adjusted downward (only to students’ benefit), but never upward. If your percentage grade satisfies the constraint in the left column, then your letter grade will satisfy the constraint in the right column of the same row. Each student’s grade is determined independently without consideration of other students’ grades.

Late Policy: Deadlines will be given with each assignment. These deadlines are strict. There will be no credit for late work. For the entire term, you have four late days. You are advised to use them conservatively. On group projects you may only use late days if all members of the group have them available, and all members of the group will be charged for each late day used. They must be used in 24-hour (integer) chunks. One or more late days can be activated on an assignment by emailing me before the assignment is due. There is no penalty whatsoever associated with using a late day. This policy may not be the same as in your other classes. You are responsible for understanding it if you choose to submit late work.

A note on inclusivity:

please treat your classmates with kindness and respect, both inside the classroom and out. Classrooms can be vulnerable environments; asking questions and expanding our understanding of new concepts requires us to reveal over and over again that we don't fully know something. It's okay to not know everything immediately! It's not okay to make people feel bad about what they don't know. This can happen even in subtle ways - see this page for a few ideas on how to help create a friendly learning environment. Our individual differences enrich and enhance our understanding of one another and of the world around us. This class welcomes the perspectives of all ethnicities, genders, religions, ages, sexual orientations, disabilities, socioeconomic backgrounds, regions, and nationalities.

Academic Honesty and Collaboration:

Any work submitted with your name on it must be your work. Any pair assignment submitted with both your name and your partner’s name must be your (plural) work. On pair assignments, you must both fully contribute to the solution that you submit. (Not precisely about academic honesty, but still good guidance: receive your partner’s ideas as enthusiastically as possible, even if they may not initially seem like the obvious choice to you.) It is each student’s responsibility to speak to me in any situation where your partner is not being of sufficient help. You are required to completely understand any solution that you submit, and, in case of any doubt, you must be prepared to explain your solution to me. If you have submitted a solution that you cannot explain to me, then you have violated this policy (and so has your partner!). You may not split up an assignment and divide the work between the partners. You must stop working with your partner if you find yourself in a situation in which continuing work would cause this requirement to be violated. You do not need my permission at the time to split up; you may do so if you genuinely are unable to work together for scheduling or other reasons. Just be very clear in your submission what work was done together and what was done on your own.

You may consult with other students to get help. You may not copy (either electronically or through transcription) other people’s work. It is of course appropriate for some ideas to be transmitted through “consulting-type” help, but code generally shouldn’t be word-for-word identical; plagiarism will not be tolerated in this class. You should never be in possession of a (paper or electronic) copy of a classmate’s code before the due date for the assignment. (Sharing code after the deadline is fine, as is sharing code with your partner.) Another way to articulate this: when getting help, that consultation should be in English and not in Python. You may refer to online manuals documenting Python functions or perform Google searches to find the syntax for a particular Python feature; you should include a comment at the relevant point in your program explaining the source you used (typically including the URL). You may bounce ideas off of classmates; if those conversations lead to insights, then you should cite your classmate. (This sharing and attribution of ideas is an example of a thriving course community!) Use your judgment and common sense to decide whether consultation with a classmate was sufficiently substantive that citation of your classmate is warranted. You should mention significant consultation with lab assistants (by name if possible!). Any amount of help that caused you to revise your program should be cited. Again: use common sense. It is never acceptable to submit a program that you have found elsewhere (via Google, a book, a friend currently/previously in this class, etc.). Changing variable names, altering indentation, and adding comments do not change the fact that such a program was not written by you. If you are ever unclear about how to represent what work you have done, (a) ask and (b) describe clearly what you have done.

Academic Accommodations:

Carleton College is committed to providing equitable access to learning opportunities for all students. The Disability Services office (Henry House, 107 Union Street) is the campus office that collaborates with students who have disabilities to provide and/or arrange reasonable accommodations. If you have, or think you may have, a disability (e.g., mental health, attentional, learning, autism spectrum disorders, chronic health, traumatic brain injury and concussions, vision, hearing, mobility, or speech impairments), please contact or call Jan Foley, Student Accessibility Specialist (x4464) or Chris Dallager, Director of Disability Services (x5250) to arrange a confidential discussion regarding equitable access and reasonable accommodations.