Building a Computational and Data Science Workforce

Under-representation of minorities and women in the STEM workforce, especially in computing, is a contributing factor to the Computational and Data Science (CDS) workforce shortage. In 2019, 12 percent of the workforce was African American, while only 7 percent of STEM workers were African American with a bachelor’s degree or higher. Hispanic share of the workforce increased to 18 percent by 2019; Hispanics with a bachelor’s degree or higher are only 8 percent of the STEM workforce [1]. Although some strides have been made in integrating CDS competencies into the university curriculum, the pace of change has been slow resulting in a critical shortage of sufficiently qualified students at both the baccalaureate and graduate levels. The NSF Working Group on Realizing the Potential of Data Science final report recommends “strengthening curriculum at EPSCoR and Minority Serving Institutions (MSI) so students are prepared and competitive for employment opportunities in industry and academia” [2]. However, the resource constraints and large teaching loads can impede the ability of MSIs and smaller institutions to quickly respond and make the necessary curriculum changes. Ohio Supercomputer Center (OSC) in collaboration with Bethune Cookman University (B-CU), Clark Atlanta University (CAU), Morgan State University (Morgan), Southeastern Universities Research Association (SURA), Southern University and A&M College (SUBR), and the University of Puerto Rico at Mayagüez (UPRM) are piloting a Computational and Data Science Curriculum Exchange (CExchange) to address the challenges associated with sustained access to computational and data science courses in institutions with high percentage enrollment of students from populations currently under-represented in STEM disciplines. The goal of the CExchange pilot is to create a network for resource constrained institutions to share CDS courses and increase their capacity to offer CDS minors and certificate programs. Over the past three years we have found that the exchange model facilitates the sharing of curriculum and expertise across institutions for immediate implementation of some courses and long-term capacity building for new Computational and Data Science programs and minors.