Democratizing Data Science Methods for Social Work Students: Report on Organizing the Summer Institute of Computational Social Science

Background and Purpose: Data science is the interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. A data science approach can assist social work researchers in identifying the root causes of complex social problems, developing evidence-based interventions, and communicating their findings. At the same time as the field of data science grows, social work researchers have highlighted the importance of social equity in these initiatives. However, data science skills are not widely taught or practiced in social work education and research. This gap between the potential and the reality of data science in social work indicates a need for training that can prepare social work students to harness the emerging power of data science for social good, consistent with the Grand Challenges. We aim to present the steps taken to implement the Summer Institute of Computational Social Science (SICSS)-NYU Silver with a focus on social equity and social good. We will also present data on the lessons learned from the perspective of student participants and provide recommendations for future efforts to train social work students in data science.

Methods: This paper reviews the literature on teaching data science for social work PhD students and other social science disciplines and uses a case study approach to describe the implementation of SICSS-NYU Silver. The study consisted of two phases: (1) a review of the literature on data science education in social work and related fields; (2) a focus group discussion with social work students (N=6) who participated in SICSS-NYU Silver to understand their experiences in part to refine the curriculum.

Results: The SICSS-NYU Silver curriculum focuses on (1) digital data collection, (2) data analysis (e.g., natural language processing, machine learning), (3) ethics, diversity, and social justice, (4) special topics, and (5) group projects. The expected outcomes of this study are an overview of the current status of data science education in social work programs from the literature review and a set of recommendations drawn from the focus group discussion among participants and organizers for teaching data science to social work students. At present, three social work programs in the US provide training in data science in the form of joint degree, certificate, or minors. The literature review yielded a set of core concepts and skills useful for social work research that incorporates data science methods, such as data collection, data visualization, and interdisciplinary communication. Based on the focus group interview with students who completed SICSS-NYU Silver, additional curricular needs and support will be identified.

Conclusions and Implications: This study will contribute to the advancement of knowledge and innovation in both data science and social work fields. It also will provide practical guidance for social work educators, administrators, and policymakers who are interested in developing or enhancing their curricula with data science content. Ultimately, this study will help prepare future generations of social workers who can leverage data science for solving the problems faced by marginalized populations.