 I'm Gina Griffin and I'm a social worker and I'm happy to speak with you about why social workers need data science. First of all, you might be wondering what the heck do social workers do and this seems to mystify nearly everybody that I meet. So the National Association of Social Workers states that the primary mission of the social work profession is to enhance human well-being and help to meet basic and complex needs of all people. With particular focus on those who are vulnerable, oppressed, and living in poverty. Social work is different from other professions because we focus both on the person and their environment. We recognize that the person is not separate from that environment and then we have to understand that entire system to help the person to meet their needs. We are trained to provide case management, psychotherapy and develop policy and oversee practice and administrative settings and also to develop research. Additionally, social workers have been fairly resistant to technology. We tend to be late adapters. Sometimes this has to do with the fact that we tend to skew a little bit older as a whole and so developing those new skills can be pretty intimidating. Often this is also because we fear that embracing technology will change the field of social work. Social workers take pride in developing a practice that values face to face interaction with clients. So the fear is often that integrating technology and relying more heavily on science and data as a whole will remove us too far from that person to person experience. The world is changing and social work has to keep up. Social workers need to understand how data is influencing the world around us and how it can positively and negatively impact our clients. We need to understand how and why tools such as predictive policing and recidivism algorithms often negatively impact the marginalized communities with which we work and as practice in the organizations in which we work is becoming more and more data driven, we need to be able to understand how to leverage that data to provide the best outcomes for the vulnerable and marginalized communities that we serve. While many social workers are rightfully enamored with big data and its impact on clients, I also see the value of teaching everyday data science to social work practitioners. We need to be able to effectively collect and interpret data from a variety of sources, such as mental health measures, which we collect over time and use and manage and measurement based care. We were from mileage logs for staff members who use fleet vehicles to provide services to clients and community. And ultimately, we need to be able to effectively interpret what that data is telling us and to communicate that to both staff and management. Right now, the preferred tool is often the Excel spreadsheet and based on interviews that I've collected, even those are sometimes a struggle for some of my colleagues. As a result for my doctoral capstone project, I'm building a website and learning management system named Adventures in Social Work Research. This focuses on teaching research skills to direct practice social workers and these are social workers who work face to face with clients. One of the primary segments of the website will be devoted to teaching our programming to social workers. I'm hoping that this is one of the truly, first truly functional parts of the website, as I believe that the demand for these types of skills will only increase over time. And I'm drawing on my skills as an R-Ladies facilitator to share this skill set with my social work colleagues. I will be completing my doctoral capstone project in October 2020. By that time, I would like to have several beginner R-Programming learning modules here towards social workers available for use. If you would like to work with me and help me to develop these modules, please feel free to contact me. I'm also happy to answer questions about the project as a whole. And I'm always happy to discuss ways in which social work can benefit from practice research and data science. Thank you.