Syllabus

Common Questions

Course Description

Welcome to your first programming course! CS 220 / CS 319 (Data Science Programming I) is a gentle introduction to coding for students outside of Computer Science. Our goal is simple: to write Python code to answer questions about real datasets. CS 220 / CS 319 will require you to practice coding a lot this semester. CS 220 students will complete 13 programming projects. CS 319 students will complete 9 programming projects and work on a self-chosen graduate-level project. It's a lot of work, but if you take this course seriously and invest the time, you'll walk away with an incredible new skill: the ability to make computers work on your behalf. Students must have access to a laptop with MAC or Windows OS, where they can install Python to complete projects for this course.

CS319 meets with CS220, and common aspects between CS319 and CS220 include lectures, quizzes, exams, and the first nine programming projects.

Additions To Syllabus Made During Semester

Readings

We'll assign readings from four primary sources this semester (all free). Stay on top of them!

Course Instructors

Lectures (Meeting Time and Location)

Lecture recordings will not be provided. In-person attendance is expected.

Instructional Modality

CS220 Grading (not applicable to CS319 students)

Your overall grade is based on the following:

CS319 Grading (not applicable to CS220 students)

Your overall grade is based on the following:

Graded Component Details

Class surveys are based on filling out surveys: including a start of the semester, end of the semester and two midsemester surveys. Corresponding assignments will be created on Canvas.

Labs: Projects: Quizzes: Exams:

Letter Grades

At the end of the semester, we will assign final grades based on these thresholds:

Letter grade ranges include decimal points, we round the scores to the hundredths place at the end of the semester.

No extra credit is given in this course.

Incompletes must be finished within 2 weeks of the conclusion of the course. Incompletes are for students who suffer from illness during the last week of class or during finals week. You must have completed P1-P12 to request an incomplete.

How to Succeed in This Course

The best way to succeed in this course would be to:

Communication Tools

  1. Piazza: Here, you can ask questions and see questions written by other students. Do not post code snippets that are more than 5 lines long. This is considered cheating!
  2. Office Hours: To visit Teaching Assistant (TA) / Peer Mentor (PM) / Instructor office hours, please see the schedule on our calendar. To access the calendar, you must be logged in with your UW-Madison credentials.
  3. Email: Email is the least preferred way of communication. A fast and easy way to get help in this course is via Office hours. If you have a question about your project grading, send an email to your Lab TA. If you don't get a response within 48 hours, please contact your instructor. Please make sure to utilize Office Hours resources prior to sending any email.
  4. Class Forms: We use several forms to collect information from you.
  5. Canvas: Grades are uploaded to Canvas. Quizzes will be on Canvas. Note that all Lecture sections have been merged into LEC001 for Canvas. We will also be sending personal messages via Canvas to convey information such as exam room assignments and exam scantron results.
  6. Course Email: You'll receive announcement emails through compsci220-*-@g-groups.wisc.edu.

Accommodations

Please notify the instructor for your lecture section within the first two weeks of classes:

Please notify your instructor if you suffer from an extended (> 1 week) illness.

Cheating

You shouldn't cheat, but what is cheating? This may not be obvious to people taking a CS course for the first time, so everybody should read this. The most common form of academic misconduct in these classes involves copying code for programming projects. Here's an overview of what you can and cannot do:

Acceptable

NOT Acceptable

Similarity Detection: of course, with 1000+ students, it's hard for a human TA to notice similar code across two submissions. Thus, we use automated tools to looks for similarities across submissions. Such similarity detection is an active area of Computer Science research, and the result is tools that detect code copying even when students methodically rename all variables and shuffle the order of their code. We take cheating detection seriously to make the course fair to students who put in the honest effort.

Generative AI: The use of artificial intelligence (AI) tools and applications (including, but not limited to, ChatGPT, DALL-E, and others) for course assignments and assessments does not support the learning objectives of this course and is prohibited. Using them in any way for this course is a violation of the course’s expectations and will be addressed through UW–Madison’s academic misconduct policy, specifically UWS 14.03(1)(b) Uses unauthorized materials or fabricated data in any academic exercise.

Citing Code: you can copy small snippets of code from stackoverflow (and other online references) if you cite them. For example, suppose I need to write some code that gets the median number from a list of numbers. I might search for "how to get the median of a list in Python" and find a solution at https://stackoverflow.com/questions/24101524/finding-median-of-list-in-python.

I could (legitimately) post code from that page in my code, as long as it has a comment as follows:

  # copied from https://stackoverflow.com/questions/24101524/finding-median-of-list-in-python
  def median(lst):
    sortedLst = sorted(lst)
    lstLen = len(lst)
    index = (lstLen - 1) // 2

    if (lstLen % 2):
      return sortedLst[index]
    else:
      return (sortedLst[index] + sortedLst[index + 1])/2.0

In contrast, copying from a nearly complete project (that accomplishes what you're trying to do for your project) is not OK. When in doubt, ask us! The best way to stay out of trouble is to be completely transparent about what you're doing.

Learning Outcomes

By the end of the course, students should be able to:

Student Grievance Procedure

Any student at UW–Madison who feels that they have been treated unfairly has the right to voice a complaint and receive a prompt hearing of the grievance. The basis for a grievance can range from something as subtle as miscommunication to the extreme of harassment. Ensuring that all students feel welcome and supported is a top priority of our department. The Department of Computer Sciences has developed procedures to handle complaints or incidents that are student-related, such as bias or mistreatment, or academic, such as a grade dispute or incident with an instructor. Information on the CS reporting procedures can be found here: https://www.cs.wisc.edu/report-an-incident/. For assistance in determining options, students can also contact the drop-in staff member within the Dean of Students Office at 608-263-5700, within Bascom Hall, Room 70, Monday–Friday, 8:30 a.m.–4 p.m.

Official Statements Required on the Syllabus

Course Credits

Requisites

Satisfied Quantitative Reasoning (QR) A requirement or declared in the Professional Capstone Program in Computer Sciences. Not open to students with credit for COMP SCI 301.

Course Designations and Attributes

Credit Hour Policy: Traditional Carnegie Definition – One hour (i.e. 50 minutes) of classroom or direct faculty/instructor instruction and a minimum of two hours of out of class student work each week over approximately 15 weeks, or an equivalent amount of engagement over a different number of weeks. This is the status quo and represents the traditional college credit format used for decades. If you have regular classroom meetings and assign homework, reading, writing, and preparation for quizzes and exams, make this choice.

Regular and Substantive Student-Instructor Interaction: Substantive interaction is engaging students in teaching, learning and assessment through at least two of the following: direct instruction, providing feedback on student work, providing information about course content, facilitating discussion of course content, or other substantive interaction. Regular interaction is: predictable and scheduled interaction with students consistent with the course length (usually at least weekly but more often in a course of short duration). Regular and substantive student-instructor interaction, as defined by the US Department of Education (Within 34 C.F.R. §600.2), is always a requirement of UW-Madison for-credit learning activities. Find more information including examples of regular and substantive instruction.

Official Course Description: Introduction to Data Science programming using Python. No previous programming experience required. Emphasis on analyzing real datasets in a variety of forms and visual communication. Recommended for Data Science majors and other majors

Course Evaluations: UW-Madison now uses an online course evaluation survey tool, AEFIS. In most instances, you will receive an official email two weeks prior to the end of the semester when your course evaluation is available. You will receive a link to log into the course evaluation with your NetID where you can complete the evaluation and submit it, anonymously. Your participation is an integral component of this course, and your feedback is important to me. I strongly encourage you to participate in the course evaluation.

Academic Integrity: By virtue of enrollment, each student agrees to uphold the high academic standards of the University of Wisconsin-Madison; academic misconduct is behavior that negatively impacts the integrity of the institution. Cheating, fabrication, plagiarism, unauthorized collaboration, and helping others commit these previously listed acts are examples of misconduct which may result in disciplinary action. Examples of disciplinary action include, but is not limited to, failure on the assignment/course, written reprimand, disciplinary probation, suspension, or expulsion.

Accommodations for Students with Disabilities: The University of Wisconsin-Madison supports the right of all enrolled students to a full and equal educational opportunity. The Americans with Disabilities Act (ADA), Wisconsin State Statute (36.12), and UW-Madison policy (Faculty Document 1071) require that students with disabilities be reasonably accommodated in instruction and campus life. Reasonable accommodations for students with disabilities is a shared faculty and student responsibility. Students are expected to inform faculty [instructors] of their need for instructional accommodations by the end of the third week of the semester, or as soon as possible after a disability has been incurred or recognized. Faculty will work either directly with the student [you] or in coordination with the McBurney Center to identify and provide reasonable instructional accommodations. Disability information, including instructional accommodations as part of a student's educational record, is confidential and protected under FERPA.

Diversity and Inclusion: Diversity is a source of strength, creativity, and innovation for UW-Madison. We value the contributions of each person and respect the profound ways their identity, culture, background, experience, status, abilities, and opinion enrich the university community. We commit ourselves to the pursuit of excellence in teaching, research, outreach, and diversity as inextricably linked goals. The University of Wisconsin-Madison fulfills its public mission by creating a welcoming and inclusive community for people from every background – people who as students, faculty, and staff serve Wisconsin and the world.