Using Python for Applied Data Science in Social Work: Introduction to Concepts, Methods, Model Building, and Critical Perspectives (Sponsored by East Carolina University)

This workshop is designed for social work researchers interested in using data science for their research and who wish to learn more about the promise and questions surrounding data science in social work. Data science is the term for a set of analytic skills and methods that span multiple disciplines, such as math, statistics, and computer science. Analytic approaches are evolving rapidly, but include classification, regression, and clustering techniques. Data science often involves working with relational databases and fitting models with large datasets.

This workshop will discuss key concepts and definitions in data science, and introduce several methods and tools using Python, one of the most effective programming languages for data science. Participants will use Google Colab, which provides a free, browser-based, and easy-to-use environment for running Python packages (e.g., NumPy, Pandas, and Matplotlib) and code. This workshop will also provide opportunities for hands-on exercises with real data, such as ensemble methods, text analysis and spatial/image analysis. The session will conclude by discussing the epistemological normative and political questions posed by social work data science.

The workshop is meant for social work researchers across all career levels, including graduate students and early to late career social work researchers in academia, government, social and health services, and NGOs. Previous experience with basic statistics, including OLS regression modeling, Generalized Linear Modeling, and some experience using statistical software (e.g., SPSS, Stata, R, Python) is recommended. However, no previous knowledge of specific data science methods in the social and health sciences is required.