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.

Predictive analytics is a set of techniques and an advanced methodological and research approach that seeks to reach conclusions about the future, rather than explanations of specific issues or phenomena. The fast growth in popularity and application of data science to different businesses and activities, such as human services and nonprofit management, are related to the emergence and consolidation of big data. In terms of digital networking, the availability of data produced by individuals every day is enormous. Tools and techniques such as machine learning, deep learning, visualization, time series analysis, networking analysis, natural language processing, and text mining may help support evidence-based practice for social workers. Predictive analytics and big data offer an opportunity to enhance innovative social change and people’s well-being.