Course Schedule

Daily

Every class session will start and end with a writing prompt that will be used for class discussions. This course will involve some lectures and speakers, but will largely involve writing exercises, discussion/reflection activities, and teamwork on the data science project. We want to cultivate a culture that respects all viewpoints and values all experiences, where there are complicated answers, and where a growth mindset is upheld.

Weekly

• Week 1-5 - will focus on training and guidance about how our work fits into the greater context of Baltimore, understanding what the CBOs do and why, as well as some instruction on data security, privacy, and ethics
• Week 6-11 - will focus on working on the data science product(s)
• Week 12-15 - will focus on plans for implementation, sustainability/maintenance, and optionally training
• Week 16 - will focus on reflection on the implementation, lessons learned, and future ideas

Class Sessions

First Term

Week 1 (9/1)

Course and Context - Historical and Present

Summary	Content	Homework (please do before week 2)	Resources
This week will focus on the rationale, structure, philosophy, and goals of the course. We will also examine resources and discuss how the historical and current context of Johns Hopkins in the Baltimore Community may influence our work.	* SOURCE Module 1: How History Impacts Our Work * Introduction Slides * Reflection Journal Template	* Black Butterfly Academy: Baltimore Apartheid * Mitigating Bias Training	Reading About History: * Greenwood Tulsa Massacre * Rosewood Massacre * Red Summer * Not in My Neighborhood: How Bigotry Shaped a Great American City * Henrietta Lacks: science must right a historical wrong More About Bias: * Discrimination in Workplace Dynamics: Toward a Structural Account of Disparate Treatment Theory * Take an Implicit Bias Test * Bias in A.I. For Women & People of Color

2 (9/8)

Critical Service-Learning

Summary	Homework (please do before week 3)	Resources
This week Tyler Derreth, the Associate Director of SOURCE, will discuss what critical service-learning is and how it differs from other forms of service.	Traditional vs Critical Service-Learning	* Product or Process? Reciprocity or Solidarity * Theoretical roots of CSL * A case study of CSL * SOURCE Dos and don’ts

3 (9/15)

Critical Reflection Practices

Summary	Content	Homework (please do before week 4)	Resources
This week we will discuss how and why there is a focus on social and historical context and not just the data science for this course. We will talk about how this context can or perhaps should influence how we perform the data science tasks planned and how we interact with community partners.	* Course Guide to Critical Reflection * What is critical reflection? * Models of reflection * Critical Reflection Review in-class Assignment	* Git & GitHub Assignment * CBO Research and Reflection Assignment	Extra Reading: * Critical Reflective Practice * Critical Reflection in Higher Education Review Extra Videos: * “What, So What, Now What” Model video * Understanding reflective practice video

4 (9/22)

Project Planning and Github

Summary	Content	Homework (please do before next week)	Resources
This week we will discuss more specifically about how students will interact with partners, as well as examples of what has and has not worked well in past similar projects. Students will meet with their project group for the first time and start assigning student roles. We will also introduce GitHub.	* Data Science Products (slides) * Project planning form * Project organization and infrastructure suggestions * Version Control with GitHub (slides)	* Shiny Gallery Assignment * Finish Project planning form	* SOURCE Dos and don’ts * Project Student Roles * Project Reminders * Intro Reproducibility Course * Advanced Reproducibility Course * Creating Minimum Viable Products in Industry-Academia Collaborations

5 (9/29)

Shiny, Flexdashboard, & Data Security/Privacy/Ethics

Summary	Content	Homework (please do before next week)	Resources
This week we will learn about Shiny, an R tool for applications and dashboards. We will also discuss topics related to data privacy, security, and ethics and how we can incorporate best practices into our projects.	* Shiny (slides coming soon) * Flexdashboard case study * Data Security, privacy, and Ethics	* Approaching Critical Service-Learning Assignment	* Welcome to Shiny * Mastering Shiny Book * Ethical Data Handling Course

6 (10/6)

Work on the project

Summary	Homework (please do before next week)	Resources
This week we will focus on working on our projects and will continue to get familiar with GitHub.	Work on the project! (Please track your work on Slack - details coming on Slack)	* SOURCE Dos and don’ts * Project Student Roles * Project Reminders

7 (10/13)

Work on the project

Summary	Homework (please do before next week)	Resources
This week we will focus on working on our projects.	Work on the project! (Please track your work on Slack - details coming on Slack)	* SOURCE Dos and don’ts * Project Student Roles * Project Reminders

8 (10/20)

Critical reflection and updates

Summary	Homework (please do before next week)	Resources
This week we will focus on working on our projects and we will check in on how things are going.	Work on the project! (Please track your work on Slack - details coming on Slack)	* SOURCE Dos and don’ts * Project Student Roles * Project Reminders

Second Term

Time	Focus	Summary
Week 9 & 10 & 11 (10/27, 11/3, 11/10)	Focus on projects!	These weeks we will focus on the products for our projects.
Week 12 (11/17)	20 min (15 min + 5 min for questions) Internal presentations	This week we will check in with the class to describe what we have done so far. The core data science products should largely be complete. Last 30 min for adjustment plan. In the following weeks we will shift focus to implementation plans and documentation.
Week 13 (12/1)	Implementation and Sustainability	These weeks we will especially focus on how the community partners will use the data project product and how they might sustain the product over time, as well as training for the community partners if they asked for it.
Week 14 (12/8)	Presentation of products to organizations	This week we will present our core data science products to the organizations. We will discuss our implementation/training plans with them and continue to work on these materials.
Week 15 (12/15)	Adjustments and Implementation	This week will we modify core products based on feedback and finalizing documentation, implementation, and training materials.
Week 16 (12/22)	Reflections, Future plans, and Wrap-up	This week we will focus on how the implementation is going, lessons learned, and thoughts about future directions for the community organization.