In this course we explore how societal issues of ethics, fairness, responsibility, and privacy affect the data science lifecycle. The data science lifecycle includes data acquisition, cleaning and pre-processing, analysis and use of data (we explore techniques from machine learning, inferential statistics, and causal inference), and communication of data science results. We will consider the steps one must follow to conduct data science tasks responsibly, and will delve into the details of fairness and privacy issues that arise along the way. The course has three components: lectures, programming assignments, and readings. Through the combination of these three, students who complete the course will learn how to conduct data science tasks responsibly, recognize fairness, privacy, and other important implications, and improve their programming and technical skillset.
Instructor: Raul Castro Fernandez (firstname.lastname@example.org)
Teaching Assistant (TA): Zhiru Zhu (email@example.com)
Lectures: Tuesday and Thursday 3:30pm–4:50pm (Central Time)
Prerequisites: CMSC/DATA 11900
Office Hours: You can use office hours to discuss any topic we cover in class. Zhiru: 2-3pm Tuesdays (JCL 236). Raul: 1:30pm - 2:30pm Thursdays in JCL 245.
Canvas Site: Go here We use Canvas mostly for announcements. All other relevant information is here.
Offline/Asynchronous discussion: We will use Campuswire for offline discussion. See the expectations on how to use Campuswire below.
The schedule is available here.
The schedule includes lecture topics, readings, as well as the dates where all assignments will be released and due. Make sure to check the schedule often, as I expect there will be some adjustments to the dates throughout the quarter.
- Programming Assignments (6 in total. 5 of them are worth 10%. One of them is worth 20%. Total 70%). The programming assignments are the most important component of your grade. You can find the tentative release dates in the schedule. You work on these individually, but you can discuss high-level ideas with other classmates. Your grade is based on the deliverables and a quiz delivered via gradescope. There is a strict no-extension policy. You can submit up to 2 days later, with a 10% penalty per day. That is, if you submit within 24 hours of the deadline, the penalty is 10% and if you submit within 48 hours, it will be 20%. Assignment submitted later than 48 hours after the deadline will not be considered.
- Reading Responses (7 in total. 2% each. Total 14%) We will assign readings each week. At the end of the week we will ask a question about the readings and ask you to provide a brief answer. There are two dedicated discussion sections throughout the quarter where we will discuss the most interesting reading responses. There is a strict no-extension policy.
- Issue Report (13%) This is an open-ended assignment. You will identify a data science result published in some form (news article, research paper, technical report, etc) and reproduce the results, identifying any potential problems related to the topics we cover in the class. Your goal is to present the original result, your strategy to reproduce it, any problems you found, any recommendations you make based on those. The project will be graded based on the correctness and completeness of the analysis, as well as based on a short report summarizing the results. We will provide a rubric to help you guide your work.
- Class Participation (3%). A small part of your grade comes from participation. If your grade ends up on a border between two grades (e.g. B+ and A-) this can sway your grade. Participation can be earned in several ways: i) being active on Campuswire, i.e., answering and commenting on questions (asking questions on Campuswire does not count) ii) actively engaging in discussion and asking questions in class.
How to ask questions and seek help
Before posting on Campuswire:
- Make sure you’ve consulted the documentation and tutorials for the software you are using. If that does not help, make sure you check online, for others who may have faced similar questions. You can use search engines for this, and stackoverflow is a great resources. Last, if your question is not resolved, you can use Campuswire.
- Aim to ask all questions publicly, so other students can answer them.
- Staff has limited budget for Campuswire. We will check it ~2 times a day. We will offer additional help during office hours.
- Aim to answer question in Campuswire, this will count towards participation in the class.
- Do not post code directly on Campuswire. And do not post screenshots of the output you obtain.
- Aim to include all relevant information with your post, so we can help you.
- We will not answer questions that do not follow the above guidelines.
Academic Integrity Policy
The University of Chicago has formal policies related to academic honesty and plagiarism, as described by the university broadly and the college specifically. We abide by these standards in this course. Depending on the severity of the offense, you risk being dismissed altogether from the course. All cases will be referred to the Dean of Students office, which may impose further penalties, including suspension and expulsion. If you have any question about whether some activity would constitute cheating, please feel free to ask. In addition, we expect all students to treat everyone else in the course with respect, following the norms of proper behavior by members of the University of Chicago community.
Student interactions are an important and useful means to master course material. We recommend that you discuss the material in this class with other students. While it is acceptable to discuss assignments in general terms, it is not acceptable to turn in someone else’s writing or code (or fragments thereof) as your own. When the time comes to write down your answer, you should write it down yourself from your own understanding. Moreover, you should cite any material discussions or written sources, e.g., “Note: I discussed this exercise with Jane Smith.” If one student “helps” another by giving them a copy of their assignment, only to have that other student copy it and turn it in, both students are culpable. If you have any questions about what is or is not proper academic conduct, please ask an instructor. (This description of academic honesty is derived in part from those of Stuart Kurtz and John Reppy).