Community Engaged Data Science Course
Why take this course?
In the community-engaged data science course, students will have the chance to use real-world data to address questions of importance to community partners.
This year, we will be working with local partners involved in public transportation policy to analyze data related to the behavior of transit riders. Public transit shapes who can reach what jobs, schools, healthcare, and opportunity. It plays an important role in a society’s environmental impact, affordability, connectivity, and safety. As such, transit is one of the clearest factors in issues of sustainability, equity, and wellbeing in modern society. The analysis of transit data is tied to real policy decisions, and students in this course will work on questions that are directly relevant to their own communities and futures.
What to expect from the course
Students in the course will select a data project from a list of options and complete that project in a self-directed manner, drawing on the skills and knowledge they have obtained through prior coursework and experiences.
Instructors will provide support for best practices in working with data and will scaffold relationships with community partners. Projects will vary in complexity and we expect to enroll students with a range of prior experience levels in terms of both data science and community engagement. However, the course does have prerequisites (see below).
Prerequisites
Students are not required to be enrolled in the UW Data Science Minor, but they must have taken at least three courses that are associated with the minor: 2 “data skills” classes and 1 “data studies” class, as defined (https://dataminor.uw.edu/curriculum/overview/) in the DS Minor requirements. Courses that count toward each of these categories can be found here: https://dataminor.uw.edu/curriculum/upcoming-courses/
Project work in this course will involve statistical analysis and computer programming (in Python or R). In terms of skill level, students should be comfortable with doing these things without instructor scaffolding:
- calculating summary statistics to get a basic sense of the “shape” of data
- conducting statistical hypothesis tests and make statements about result validity and confidence levels
- using a programming language to join two tables of data together based on an ID field
- using a programming language to draw a histogram
Grading
This course is offered on a credit/no credit basis. To receive credit, students must participate in class activities (e.g. discussions, stakeholder meetings and code audits), turn in weekly assignments (e.g. readings and project journal entries), and complete project deliverables in the form of a final portfolio (e.g. code, documentation, visualizations).
Credit & Time Commitment
This is a 5-credit course, corresponding to a total time commitment [https://www.washington.edu/students/reg/credit.html] of ~15 hours/week.
Learning Objectives
- Describe the problem space the community partner is working in and articulate their data challenges.
- Translate technical and domain questions about data across a range of stakeholders with varying areas of expertise.
- Articulate and balance the different interests and needs of various stakeholders in the projects while attending to the distribution of power across the stakeholder landscape.
- Implement processes for engaging responsibly and collaboratively with a community partner in a way that attends to their unique positionality and centers their concerns.
- Assess critical and ethical considerations at different stages of the data science life-cycle and project development.
- Appraise and select methods that are well suited to research questions and sources of data most salient to community partner(s).
- Anticipate and communicate limitations of projects in a manner accessible to community partners, including legacies of data work and uncertainties in results.
- Develop and convey an analytically supported story with data that incorporates multiple forms of evidence and is responsive and legible to the project’s community partner.
Enrollment
Students will enroll in GEN ST 345: Community Engagement Practicum (Section B). Registration requires an add code, which will be provided to students who are selected for enrollment through an application process. As part of the application process, you will be asked to provide information about your completion of prerequisites, upload your (unofficial) transcript, and respond to three essay questions.
When you are ready to apply, please complete this form.
Instructors
Naomi Alterman, Assistant Teaching Professor, Allen School of Computer Science & Engineering
Anissa Tanweer, Senior Social Scientist, eScience Institute