Data science for social work practice

By Oscar Cariceo, Murali Nair and Jay Lytton

Abstract
Data science is merging of several techniques that include statistics, computer programming, hacking skills, and a solid expertise in specific fields, among others. This approach represents opportunities for  social work research and intervention. Thus, practitioners can take advantage of data science methods and reach new standards for quality performances at different practice levels. This article addresses key terms of data science as a new set of methodologies, tools, and technologies, and discusses machine learning techniques in order to identify new skills and methodologies to support social work interventions and evidence-based practice. The challenge related to data sciences application on social work practice is the shift on the focus of interventions. Data science supports data-driven decisions to predict social issues, rather than providing an understanding of reasons for social problems. This can be both a limitation and an opportunity depending on context and needs of users and professionals.

Keywords: Big datadata sciencemachine learningmanagementresearchsocial work

Author Biographies

Oscar Cariceo, MSW, Adjunct faculty in the School of Social Work at Universidad Central de Chile.

Murali Nair, PhD, Clinical Professor of Social Work Dept. of Social Change and Innovation, University of Southern California.

Jay Lytton, MBA, MSW, Lead Project Manager – Enterprise Resource Forecasting and Allocation at Amgen Company.

“NSWM Policy Fellows program, though it is only for one year, its impact for the mentor and the mentee is life long. Though I mentored Osar from Chile as an International Policy Fellow two years ago, since then we presented multiple papers at conferences, published an article and writing more”
– Murali Nair, PhD

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