Assignments are comprised of weekly in-class activities, weekly reading reflections, written assignments, and programming/data analysis assignments. Weekly in-class reading groups will discuss the assigned readings from the course and students are expected to have read the material in advance. In class activities each week are posted to Canvas and may require time outside of class to complete.
Unless otherwise noted, all assignments are due before 5pm on the following week's class.
Unless otherwise noted, all assignments are individual assignments.
Assignment timeline
- Weekly: In-class activity output posted to Canvas (group or individual)
- Weekly: Reading reflections posted to Canvas (individual)
- A1 (due Week 3): Data privacy (programming/analysis)
- A2 (due Week 4): Data curation (programming/analysis)
- A3 (due Week 5): Sources of bias in data (programming/analysis)
- A4 (due Week 6): Auditing algorithms (programming/analysis, written)
- A5 (due Week 7): Final project plan (written)
- A6 (due Week 9): Crowdwork ethnography (written)
- A7 (due Week 11): Final project presentation (oral, written)
- A8 (due by 11:59pm on Sunday, December 10): Final project report (programming/analysis, written)
Weekly in-class activities
Love it or hate it, teamwork is an integral part of data science practice (and work in general). During each class session, you will be asked to participate in one or more group activities. These activities may involve reading discussions, group brainstorming activities, collaborative coding or data analysis, working together on designs, or offering peer support.
In each class session, one in-class activity will have a graded deliverable that is due the next day. The sum of these deliverables constitutes your participation grade for the course. The deliverable is intended to be something that you complete (and ideally, turn in, in class), but in rare cases may involve some work after class. It could be as simple as a picture of a design sketch you made, or notes from a group brainstorm. When you and your group complete the assigned activity, follow the instructions below to submit the activity and get full credit.
- Instructions
- Do the in-class activity
- Choose a group member to submit the deliverable
- Submit the deliverable via Canvas, in the format specified by the instructor within 24 hours of class
- Make sure to list the full names of all group members in the Canvas post
Late deliverables will never be accepted, and everyone in the group will lose points. So make sure you choose someone reliable to turn the assignment in!
Weekly reading reflections
This course will introduce you to cutting edge research and opinion from major thinkers in the domain of human centered data science. By reading and writing about this material, you will have an opportunity to explore the complex intersections of technology, methodology, ethics, and social thought that characterize this budding field of research and practice. As a participant in the course, you are responsible for intellectually engaging with all assigned readings and developing an understanding of the ideas discussed in them.
This assignment is designed to encourage you to reflect on these readings (or in some cases, viewings or listenings) and make connections during our class discussions. To this end, you will be responsible for posting reading reflections every week of the quarter (except for week 1).
There will generally be multiple readings assigned each week. You are responsible for reading all of them. However, you only need to write a reflection on one reading per week. Unless your instructor specifies otherwise, you can choose which reading you would like to write your reflection about.
These reflections are meant to be brief but meaningful. Follow the instructions below, demonstrate that you engaged with the material, and turn the reflection in on time, and you will receive full credit. Late reading reflections will never be accepted.
- Instructions
- Read all assigned readings.
- Select a reading to reflect on.
- In at least 2-3 full sentences, answer the question "How does this reading inform your understanding of human centered data science?"
- Using full sentences, list at least 1 question that this reading raised in your mind.
- Post your reflection to Canvas before the next class session.
You are encouraged, but not required, to make connections between different readings (from the current week, or previous weeks) in your reflections.
Grading for 5-point assignments
A1, A2, A3, A4, and A5 are worth 5 points each. The following grading scheme will be used to evaluate those assignments. If you have questions about how your assignment was graded, please see the TA or instructor.
- 5 points - Above and beyond
- The student exceeded the requirements of the assignment and demonstrated novelty beyond what was defined.
- 4 points - Competent and confident
- The student competently and confidently addressed requirements to a good standard.
- 3 points - Acceptable
- The student met the minimum requirements for the assignment.
- 2 points - Partial
- The student submitted something, but only addressed some of the assignment requirements or submitted work that was poor overall.
- 1 point - The student submitted something.
