Guide to Computational Skills

Version 1. January 29, 2021

Computational skills encompass computational physics skills (e.g., translating models into code, choosing scales and units, choosing appropriate algorithms and tools, extracting physical insight, understanding the limitations of computers and computer models), the use of a variety of computational tools (e.g., spreadsheets, structured programming languages, computer-based symbolic manipulations, modeling packages), and technical computing skills (e.g., analysis, visualization, and presentation of data). Computational skills can be introduced at numerous points throughout the curriculum through individual and group activities, and can become more sophisticated as students progress through their education.


Computational tools and techniques are used ubiquitously in physics, are integral to how physics is currently practiced, and provide excellent preparation for careers. Knowledge and skills in programming, simulations, and modeling are needed by physics graduates in a variety of careers. Adding these skills to the curriculum addresses a common weakness that many physics graduates report in their undergraduate programs, improves and accelerates students’ ability to engage in research and solve research-like problems, and may assist in the recruiting of students. Computational skills allow students to answer questions not solvable through analytic techniques, including practical and applied problems, and are transferable across disciplines. The use of computational tools can deepen students’ understanding of fundamental concepts and principles.

Effective Practices

  1. Establish goals and a plan for providing students with computational skills

  2. Integrate opportunities to develop computational skills into the curriculum

  3. Provide students early and continuing opportunities to learn and apply computational skills

  4. Communicate the value of computation in physics and for a broad range of careers

Programmatic Assessments

The Cycle of Reflection and Action

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This material is based upon work supported by the National Science Foundation under Grant Nos. 1738311, 1747563, and 1821372. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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