Difference between revisions of "Human Centered Data Science (Fall 2018)/Assignments"

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;Scheduled assignments
;Scheduled assignments
* '''A1 - 5 points''' (due 10/11): Data curation (programming/analysis)
* '''A1 - 5 points''' (due 10/18): Data curation (programming/analysis)
* '''A2 - 10 points''' (due 11/1): Sources of bias in data (programming/analysis)
* '''A2 - 10 points''' (due 11/1): Sources of bias in data (programming/analysis)
* '''A3  - 10 points''' (due 11/15): Mechanical Turk Ethnography (written)
* '''A3  - 10 points''' (due 11/15): Crowdwork Ethnography (written)
* '''A4 - 10 points''' (due 11/22): Final project plan (written)
* '''A4 - 10 points''' (due 11/22): Final project plan (written)
* '''A5 - 10 points''' (due 12/6): Final project presentation (oral, slides)
* '''A5 - 10 points''' (due 12/6): Final project presentation (oral, slides)

Revision as of 22:55, 16 September 2018

This page is a work in progress.

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

Assignments due every week
  • In-class activities - 2 points (weekly): In-class activity output posted to Canvas (group or individual) within 24 hours of class session.
  • Reading reflections - 2 points (weekly): Reading reflections posted to Canvas (individual) before following class session.

Scheduled assignments
  • A1 - 5 points (due 10/18): Data curation (programming/analysis)
  • A2 - 10 points (due 11/1): Sources of bias in data (programming/analysis)
  • A3 - 10 points (due 11/15): Crowdwork Ethnography (written)
  • A4 - 10 points (due 11/22): Final project plan (written)
  • A5 - 10 points (due 12/6): Final project presentation (oral, slides)
  • A6 - 15 points (due 12/9): Final project report (programming/analysis, written)

more information...

Weekly in-class activities

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/or your group complete the assigned activity, follow the instructions below to submit the activity and get full credit.

Love it or hate it, teamwork is an integral part of data science practice (and work in general). During some class sessions, 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.

Instructions (individual activity)
  1. Do the in-class activity
  2. Submit the deliverable via Canvas, in the format specified by the instructor within 24 hours of class
Instructions (group activity)
  1. Do the in-class activity
  2. Before the end of class, choose one group member to submit the deliverable for the whole group
  3. The designated group member will submit the deliverable via Canvas, in the format specified by the instructor within 24 hours of class
  • Note: Make sure to list the full names of all group members in the Canvas post!

Late deliverables will never be accepted, and in the case of group activities, 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.

The weekly reading reflections assignment is designed to encourage you to reflect on these works and make connections during our class discussions. To this end, you will be responsible for posting reflections on the previous week's assigned reading before the next class session.

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 reflect on.

These reflections are meant to be succinct 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.

  1. Read all assigned readings.
  2. Select a reading to reflect on.
  3. In at least 2-3 full sentences, answer the question "How does this reading inform your understanding of human centered data science?"
  4. Using full sentences, list at least 1 question that this reading raised in your mind, and say why the reading caused you to ask this question.
  5. 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, from previous weeks, or other relevant material you've read/listened to/watched) in your reflections.

Scheduled assignments

This section provides basic descriptions of all scheduled course assignments (everything you are graded on except for weekly in-class activities and reading reflections). The instructor will make specific rubrics and requirements for each of these assignments available on Canvas the day the homework is assigned.

A1: Data curation

to come

A2: Bias in data

to come

A3: Mechanical Turk ethnography

For this assignment, you will go undercover as a member of the Amazon Mechanical Turk community. You will preview or perform Mechanical Turk tasks (called "HITs"), lurk in Turk worker discussion forums, and write an ethnographic account of your experience as a crowdworker, and how this experience changes your understanding of the phenomenon of crowdwork.

A4: Final project plan

For examples of datasets you may want to use for your final project, see HCDS_(Fall_2017)/Datasets.

For this assignment, you will write up a study plan for your final class project. The plan will cover a variety of details about your final project, including what data you will use, what you will do with the data (e.g. statistical analysis, train a model), what results you expect or intend, and most importantly, why your project is interesting or important (and to whom, besides yourself).

A5: Final project presentation

For this assignment, you will give an in-class presentation of your final project. The goal of this assignment is to demonstrate that you are able to effectively communicate your research questions, methods, conclusions, and implications to your target audience.

A6: Final project report

For this assignment, you will publish the complete code, data, and analysis of your final research project. The goal is to demonstrate that you can incorporate all of the human-centered design considerations you learned in this course and create research artifacts that are understandable, impactful, and reproducible.