SC21 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Building the Computational and Data Science Workforce


Workshop:Eighth SC Workshop on Best Practices for HPC Training and Education

Authors: Katharine Cahill (Ohio Supercomputer Center), Linda Akli (Southeastern Universities Research Association (SURA)), Tandabany Dinadayalane (Clark Atlanta University), and Ana Gonzalez (University of Puerto Rico at Mayaguez)


Abstract: 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 2011, 11 percent of the workforce was African American, while only 6 percent of STEM workers were African American. Although the Hispanic share of the workforce increased from 3 percent in 1970 to 15 percent in 2011; Hispanics were only 7 percent of the STEM workforce in 2011. 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. However, the resource constraints and large teaching loads can be a more significant barrier at Minority Serving Institutions (MSIs) and smaller institutions; impeding their ability to quickly respond and make the necessary curriculum changes. Seven institutions, including 5 MSIs, are piloting a Computational and Data Science Curriculum Exchange (C2Exchange) to address the curriculum 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 C2Exchange 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. 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.





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