Computational and Data Science Education Competencies

A number of universities, supercomputer centers, research centers, national laboratories, and industries are working on programs to foster a workforce that understands the concepts, tools, and skills to apply modeling, simulation, and data analytics to address challenging problems in science, engineering, business, social sciences, and the humanities. As a guide to establishing degree and certificate programs that impart these skills, the XSEDE project has worked with the community to develop competencies that guide the contents of such programs. To date, we have developed competencies for undergraduate and graduate level computational science and for basic and advanced data driven science. Follow the links below to see each of these sets of competencies.

Undergraduate Level Computational Science Competencies

The undergraduate competencies were originally developed as part of a project at the Ohio Supercomputer Center sponsored by the National Science Foundation. They define a minimum set of skills in several broad areas of modeling and simulation, mathematics, and computer science needed to understand and use computational modeling tools. You can see those competencies by clicking

Graduate Level Computational Science Competencies

A similar set of competencies is under development for graduate programs. Those competencies assume that the basic competencies for undergraduates have been met and additional expertise is required to produce graduates with a deeper understanding of modeling and simulation tools and the related mathematics and computer science skills. Those competencies can be viewed by by clicking

Basic Data Driven Science Competencies

With the assistance of experts from a variety of fields, we have assembled a set of basic competencies for those engaging in data driven discovery in science and other fields. Those competencies include skills relating to data organization and management, analytical techniques, and the legal, ethical, and technical questions arising from collecting, analyzing, and sharing large datasets. These competencies can be viewed by clicking

Advanced Data Driven Science Competencies

Experts from around the nation have also helped us to begin to define more advanced competencies in specialized areas of data driven science. At present these include Infrastructure and Systems, Data Management and Curation, and Knowledge Representation and Analysis. Click here to view those competencies.

Competencies for Undergraduate Computational Physics

These are a set of draft competencies for undergraduate Computational Physics. Richard Gass drew up a first draft. Rubin Landau, Jan Tobochnik, Bob Panoff, Wolfgang Christian, David Joiner, and Tim Sullivan reviewed the draft and provided many valuable suggestions that were incorporated in the second draft. The document was further refined based on an online meeting attended by Richard Gass, Wolfgang Christian, Larry Engelhardt, David Joiner, and Tim Sullivan. Click here to view these competencies.

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