By Oscar Cariceo, Murali Nair and Jay Lytton
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.
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.