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Program accreditation in medical or religious professions has existed since the 1800s while accreditation of business and engineering programs started in the early twentieth century. With this long history, these disciplines have focused on ensuring the competence of their graduates, as modern society demands appropriate expertise from doctors and engineers before letting them practice their profession. In computing, however, professional accreditation started in the last decades of the twentieth century only after computer science, informatics, and information systems programs became widespread. At the same time, although competency-based learning has existed for centuries, its growth in computing is relatively new, resulting from recent curricular reports such as Computing Curricula 2020, which have defined competency comprising knowledge, skills, and dispositions. In addition, demands are being placed on university programs to ensure their graduates are ready to enter and sustain employment in the computing profession. This work explores the role of accreditation in forming and developing professional competency in non-computing disciplines worldwide, building on this understanding to see how computing accreditation bodies could play a similar role in computing. This work explores the role of accreditation in forming and developing professional competency in non-computing disciplines worldwide, building on this understanding to see how computing accreditation bodies could play a similar role in computing. Its recommendations are to incorporate competencies in all computing programs and future curricular guidelines; create competency-based models for computing programs; involve industry in identifying workplace competencies, and ensure accreditation bodies include competencies and the assessment in their standards. 

High Performance Computing (HPC) is the ability to process data and perform complex calculations at extremely high speeds. Current HPC platforms can achieve calculations on the order of quadrillions of calculations per second with quintillions on the horizon. The past three decades witnessed a vast increase in the use of HPC across different scientific, engineering and business communities, for example, sequencing the genome, predicting climate changes, designing modern aerodynamics, or establishing customer preferences. Although HPC has been well incorporated into science curricula such as bioinformatics, the same cannot be said for most computing programs. This working group will explore how HPC can make inroads into computer science education, from the undergraduate to postgraduate levels. The group will address research questions designed to investigate topics such as identifying and handling barriers that inhibit the adoption of HPC in educational environments, how to incorporate HPC into various curricula, and how HPC can be leveraged to enhance applied critical thinking and problem-solving skills. Four deliverables include: (1) a catalog of core HPC educational concepts, (2) HPC curricula for contemporary computing needs, such as in artificial intelligence, cyberanalytics, data science and engineering, or internet of things, (3) possible infrastructures for implementing HPC coursework, and (4) HPC-related feedback to the CC2020 project. 

As data science is an evolving field, existing definitions reflect this uncertainty with overloaded terms and inconsistency. As a result of the field’s fluidity, there is often a mismatch between what data-related programs teach, what employers expect, and the actual tasks data scientists are performing. In addition, the tools available to data scientists are not necessarily the tools being taught; textbooks do not seem to meet curricular needs; and empirical evidence does not seem to support existing program design. Currently, the field appears to be bifurcating into data science (DS) and data engineering (DE), with specific but overlapping roles in the combined data science and engineering (DSE) lifecycle. However, curriculum design has not yet caught up to this evolution. This working group report shows an empirical and data-driven view of the data-related education landscape, and includes several recommendations for both academia and industry that are based on this analysis.