Social Work Data Corps Initiative Service

Overview: Data science in social work is emerging as a new research approach for exploring, analyzing, and predicting social issues. The aim of the project is to develop a data science curriculum to integrate methodologies to explore and analyze “Big Data” in social science research for social work students.Proposal text:

Background:

Data science can play a significant role in how social work students collect and use data. Data science is defined as a research tool that allows the exploration and quantitative analysis of a wide range of available data, both structured and unstructured, to develop understanding, extract knowledge, and formulate actionable results (Cariceo, Nair & Lytton, 2018).  

Data science in social work is emerging as a new research approach for exploring, analyzing, and predicting social issues at the micro, mezzo, and macro level. However, data science is noticeably absent from accredited social work programs and is not often discussed in the social work literature. Data science has the potential to bring new understandings and provide the best outcomes for our communities, especially African American communities.

The aim of the project is to develop a data science curriculum to integrate methodologies to explore and analyze “Big Data” in social science research for social work students. Specific aims include identifying the resources requirements for social work students to learn, understand, and apply data science methodologies for client systems intervention; outlining assignments that will support students throughout the Data science project, and identifying the timeframe for successful completion within three consecutive semesters.

Methods:

Four faculty participated in and completed the 3 HarvardX data science courses during the summer of 2020 and 2021. Faculty began the self-paced online courses on June 1, 2021, and ended on July 30, 2021. The courses included Data Science R-Basics, Data Visualization, Data Science Productivity Tools, and Data Wrangling. The faculty committed 10-15 hours a week learning concepts, completing assignments, and participating in zoom meetings to discuss course content and assignments. Although the courses were self-paced, the faculty completion of the course modules prior to the specified dates led to robust discussions during the Zoom meetings. Upon completion of the courses, the faculty received certificates of completion.

Results

The faculty integrated knowledge obtained in the data science training courses in the design of statistics and methods of social research, practice competencies, community-based health care, and research methods courses for undergraduate and graduate students in the Fall 2021 semester.

The faculty developed data science modules and assignments in their courses. Examples of the data science assignments that were incorporated in the courses are “Using R and RStudio;” “Data Science Project Presentation,” “Capstone Assignment on Data Science,” and “Community Needs Assessment Assignment.” The faculty will share their experience incorporating data science in their courses.

Conclusion

The findings from faculty completing the data science course was that unilaterally each person saw the benefit and enhancement of incorporating an additional research tool into classroom activities. Within the social work profession, evidence-based research is an intricate piece to all students’ practicum/internship experiences. By faculty learning and now incorporating the knowledge from the data science tools within class assignments in the statistics and methods of social research, practice competencies, community-based health care, and research methods courses students can have hands-on experience and some understanding of the use and application in practice research.