Course Description

This course is focused on helping students learn how to apply their data science knowledge in non-academic settings. Students will partner with community-based organizations (CBOs) in Baltimore to work on a data science related goal. Students will focus on developing critical service learning and professional skills as they work with CBOs to accomplish a data science related goal.

Logistics

Communication will mainly occur through Slack and we will email you about how to connect to Slack.

Instructors

Requirements

Students are required to have prior experience with R programming (or other languages) and need to have taken the following data science courses (or have equivalent experience):

140.711 Advanced Data Science I and 140.712 Advanced Data Science II

Instructor permission is required to enroll in the course.

Expectations of Students

Students will work with CBO partners as a team (3-5) for the following deliverables:

In addition to working on data science projects, students are also expected to individually hone their critical service learning skills and professional skills. This involves participating in written exercises and discussions, as well as active work to stay organized and maintain communication with teammates and the CBO.

See the project page for more information about project guidelines.

Expectations of CBOs

Partners will need to provide students with details on the project goals and uses, access to relevant data (could be de-identified or simulated if privacy is an issue), feedback about whether their goals are being met, and time and willingness to learn about how to use and possibly maintain the data product that the students work with them to create.

Student Learning Objectives

Students who complete the course will be able to:

  1. Describe the history of Johns Hopkins and the biostatistics department in the Baltimore community. Evaluate and discuss how this historical context and the current context influences our interactions with community members.
  2. Critically reflect on their own service-learning project work and how their role as a statistician impacts the CBO, as well as the greater context of society and social structures (not just the course project).
  3. Articulate what critical service learning is and describe how it differs from traditional community service endeavors. Evaluate and discuss how other methods can cause harm to communities.
  4. Understand more about how to successfully navigate challenges that occur when applying data cleaning, wrangling, analysis, collection planning, visualization, and communication skills to problems in non-academic settings, as the students create (with the guidance and help of the CBO) data science products for the goals of the assigned CBO project.
  5. Work with others to create an implementation plan for the data science products that is viable and useful for the respective CBO members. Work with stakeholders at a high level to understand their needs as well as the limitations of the data.
  6. Use teaching methods to teach others from various backgrounds and experiences to use data science techniques to work with data and communicate effectively about data.
  7. Plan for CBO to continue to work with data science products in a sustainable way even after projects are over.