Data science provides the underlying infrastructure to tackle complex societal problems through important aspects of data generation, collection, processing, storage, management, and interpretation. These data science skills are critical to developing descriptive, generative, diagnostic, and predictive models with expansive underlying data. In addition, data science expertise is the cornerstone of any well-rounded artificial intelligence (AI) solution. Foundational data science skills play a critical role in ethical data use, training models with minimal bias, and ensuring participation from all parts of society in the development of these solutions. Yet many of our government and community organizations, research areas and industry lack these skills. In this paper, we analyze the process of adoption and translation of a lower-division data science education curriculum and supporting ecosystem to the University of Maryland Baltimore County (UMBC), adopted from the University of California Berkeley model of the Data8 course. The aim of this work is to understand the student experience with the adaptation at UMBC with the contrast of the institutional contexts and to assess if the course motivated students to pursue data science careers. These curricular pathways support student learning and translation of these skills to much needed data science careers.

We discuss and evaluate qualitative interview data to identify lessons learned that have implications for other efforts at adaptation and translation of data science pedagogy and curricula. In a prior study we evaluated the challenges and positive experiences of the students experienced in the class. In this current study we wanted to understand whether and how the class empowered the students to consider data science pathways. We specifically address the research question: How does the IS 296 class and the ecosystem at UMBC impact students' career paths?

Our assessment of data from focus groups across 19 students indicates themes emerging such as role of the support network, data science as a tool or subject. We also explore pathways of students who took the IS 296 class vs students who go on to become data science scholars where they are peer mentors to the IS 296 foundational students.

Keywords: Data science, peer mentoring, career pathways.