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This Work-In-Progress paper discusses the design of two surveys as part of a study to investigate the impact of Engineers Without Borders USA (EWB-USA) undergraduate experiences on professional preparation. The overall investigation will use a sequential mixed-methods approach consisting of surveys followed by interviews for each of two populations: alumni of EWB-USA undergraduate experiences and individuals who have interacted professionally with EWB-USA alumni. The surveys are of a retrospective design, based on a number of frameworks from ABET, ASCE, and the community-engagement literature. These non-psychometric instruments were iteratively developed in collaboration with a wide range of stakeholders and tested to support their validity. Anticipated results include descriptive statistics, informational graphics, and group comparisons. This paper aims to create awareness of the overall study and provide a platform to share information related to the surveys in a timely manner. 

Prompted by the skyrocketing demand for data scientists, progress made by the ACM Data Science Task Force on defining data science competencies, and inquiries about data science accreditation, ABET is in the process of developing accreditation criteria for undergraduate data science programs. The effort is led by members of a joint data science criteria subcommittee appointed by ABET’s Computing Accreditation Commission (CAC) and CSAB (the lead society for computing accreditation). Establishing data science accreditation criteria is a notable milestone in the maturing data science discipline, indicating the presence of an accepted body of knowledge, standards of practice, and ethical codes for practitioners. This position paper motivates the effort and discusses prior work towards defining data science education requirements. It describes the ongoing process for creating and obtaining approval of the accreditation criteria, and how feedback was and will be solicited from the computing and statistical communities. The current draft data science criteria, which was approved in July 2020 by the relevant ABET bodies for a year of public review and comment, is presented. These criteria emphasize the three pillars of data science: computing foundations, mathematical/statistical foundations, and experience in at least one data application domain. This report thus serves both to inform and to stimulate the academic discussion needed to finalize appropriate data science accreditation by ABET.