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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. 

Abstract Sociologists use the concept of narrative as an analytical tool and theoretical concept to understand the stories that people tell and their role in social and cultural life. A key tenet of prior research on narratives is their capacity to shape the audience’s understanding and evaluation of the narrator. In this mixed-method study, we investigate the role of narratives in destigmatization through the case of criminal record stigma in the labor market. Based on evidence from a survey experiment in which people with managerial experience were randomly assigned to job applicants with different narratives, we show that evaluations differ across reentry narratives. Drawing on prior theorizations and qualitative interviews with employers, we identify and describe three processes through which narratives impact evaluation and destigmatization: moral justification, social affinity signaling, and information salience. 

Modern computational text classification methods have brought social scientists tantalizingly close to the goal of unlocking vast insights buried in text data—from centuries of historical documents to streams of social media posts. Yet three barriers still stand in the way: the tedious labor of manual text annotation, the technical complexity that keeps these tools out of reach for many researchers, and, perhaps most critically, the challenge of bridging the gap between sophisticated algorithms and the deep theoretical understanding social scientists have already developed about human interactions, social structures, and institutions. To counter these limitations, we propose an approach to large-scale text analysis that requires substantially less human-labeled data, and no machine learning expertise, and efficiently integrates the social scientist into critical steps in the workflow. This approach, which allows the detection of statements in text, relies on large language models pre-trained for natural language inference, and a “few-shot” threshold-tuning algorithm rooted in active learning principles. We describe and showcase our approach by analyzing tweets collected during the 2020 U.S. presidential election campaign, and benchmark it against various computational approaches across three datasets.