Core empirical concepts and skills for computer science

Abstract

Educators are increasingly acknowledging that practical problems in computer science demand basic competencies in experimentation and data analysis. However, little effort has been made towards explicitly identifying those empirical concepts and skills needed by computer scientists, nor in developing methods of integrating those concepts and skills into CS curricula. In this paper, we identify a core list of empirical competencies and motivate them based on established courses outside of computer science, their potential use in standard CS courses, and their application to real-world problems. Sample assignments that facilitate the integration of these competencies into the CS curriculum are also discussed.

Enriching introductory programming courses with non-intuitive probability experiments component

“…The concept of probability was listed as one of the core empirical competencies for computer science in [3]. The random walks example was discussed in [2] and [19].…”

A course on simulation, probability and statistics

“…While the short list of topics above falls well short of a full college-level prob/stat course, several recent SIGCSE papers have argued for greater emphasis on core empirical competencies [2,3,8]. The rest of this paper will assume that learning prob/stat is worthwhile; my goal is to convince you that combining them with simulation is a good idea.…”