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To broaden participation and diversity in data science, educators are increasingly leveraging the adaptation and sharing of successful course models. This paper presents our experience implementing a foundational data science course, adapted from the University of California, Berkeley's Data 8 Foundations of Data Science, at Northeastern University's Oakland Campus in Spring 2024. A key objective was to cultivate student engagement and demonstrate the relevance of data science across disciplines. We assessed the impact of this adaptation on a cohort of first-year students, all non-data-science majors with limited prior programming or statistical experience. Our evaluation focused on student engagement, academic trajectory, and the course's ability to spark sustained interest in data science. The results demonstrate a significant positive impact: 44% of students declared a major in data science or a combined major (e.g., data science and business or economics), 16% pursued a minor in data science, and 16% transitioned to computer science. These outcomes emphasize the importance of designing introductory data science curricula to serve diverse student populations. By incorporating real-world applications from health, economics, social sciences, entertainment, sports, and finance, students gained a deeper understanding of the field's potential and their own capacity to contribute. Furthermore, smaller class sizes promoted interactive learning and personalized assignments, creating a more engaging and accessible educational experience. This approach effectively strengthens students' comprehension of data science pathways and cultivates motivation, ultimately contributing to a more inclusive and diverse data science workforce. 

With the emergence of data science as an inherently multidisciplinary subject, there is increasing demand for graduates with well-rounded competence in computing, analytics, and communication skills. However, in conventional education systems, computing & quantitative, and communication skills are often taught in different disciplines. Data storytelling is constructing and presenting data stories to highlight the analytical insights to achieve the communication goals to a specific audience. Digital data storytelling leverages digital storytelling techniques and best practices in communication to deliver stories that can be shared in digital formats to a wide audience. In this paper, we describe and reflect on a semester-long project-based learning pilot using Digital Storytelling as a framework to allow students to explore topics themed around human flourishing and sustainability with the end goal of constructing data stories delivered in digital or video format (i.e., Digital Data Storytelling). The pilot work was conducted in an introductory data science course at a 4-year Minority Serving Institution in collaboration with students studying non-STEM disciplines at a partner community college. Our pilot demonstrates the potential benefit of this sustainability-aware Project-Based Learning design in raising students’ awareness of sustainability issues, increasing confidence in cross-disciplinary communication competency, and at the same time deepening their understanding of data science concepts. We further reflect on the significant role of an effective program model as well as challenges and opportunities for building transdisciplinary communication competency to prepare for a diverse data science workforce. 

First-generation college students and those from ethnic groups such as African Americans, Latinx, Native Americans, or Indigenous Peoples in the United States are less likely to pursue STEM-related professions. How might we develop conceptual and methodological approaches to understand instructional differences between various undergraduate STEM programs that contribute to racial and social class disparities in psychological indicators of academic success such as learning orientations and engagement? Within social psychology, research has focused mainly on student-level mechanisms surrounding threat, motivation, and identity. A largely parallel literature in sociology, meanwhile, has taken a more institutional and critical approach to inequalities in STEM education, pointing to the macro level historical, cultural, and structural roots of those inequalities. In this paper, we bridge these two perspectives by focusing on critical faculty and peer instructor development as targets for inclusive STEM education. These practices, especially when deployed together, have the potential to disrupt the unseen but powerful historical forces that perpetuate STEM inequalities, while also positively affecting student-level proximate factors, especially for historically marginalized students.