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The Best Academic Computer Science Depts: A Ranking Case Study

In: Algorithmic Decision Making with Python Resources

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  • Raymond Bisdorff

Abstract

In this case study, we are solving with our Digraph3 resources a ranking decision problem based on published data from the Times Higher Education (THE) World University Rankings 2016 by Computer Science (CS) subject. Several hundred academic CS Departments, from all over the world, were ranked that year following an overall numerical score based on the weighted average of five performance criteria: Teaching (the learning environment, 30%), Research (volume, income and reputation, 30%), Citations (research influence, 27.5%), International outlook (staff, students, and research, 7.5%), and Industry income (innovation, 5%). To illustrate our Digraph3 programming resources, we shall first have a look into the THE multiple-criteria ranking data with short Python scripts. In a second section, we shall relax the commensurability hypothesis of the ranking criteria and show how to similarly rank with multiple incommensurable performance criteria of solely ordinal significance. A third section is finally devoted to introduce quality measures for qualifying ranking results.

Suggested Citation

  • Raymond Bisdorff, 2022. "The Best Academic Computer Science Depts: A Ranking Case Study," International Series in Operations Research & Management Science, in: Algorithmic Decision Making with Python Resources, chapter 0, pages 165-185, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-90928-4_13
    DOI: 10.1007/978-3-030-90928-4_13
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