Information Systems Education Journal


V18 N3 Pages 18-27

Jun 2020

Developing an Undergraduate Data Analytics Program for Non-Traditional Students

Lionel Mew
University of Richmond
Richmond, VA USA

Abstract: This paper discusses a new educational approach to develop competencies for the future STEM workforce, and to build knowledge on success factors for educating a non-traditional target population in data competencies. It is widely accepted that a data capable workforce is critical to science and industry. The literature suggests that the need for data science and data analytics competencies in industry and academia is accelerating at a rapid pace. At the same time, census and demographic data predict that the pool of traditional college age students will continue to decrease. To meet the increasing demand for a data capable workforce, it is essential to leverage the non-traditional student pool, reskilling and upskilling the current workforce, simply because the traditional student output is nowhere near sufficient to meet the need. The current work is to develop a new program designed to provide adult learners with bachelor’s degrees and post baccalaureate certificates in Data Analytics. This results in upskilling or reskilling the existing workforce to add value to industry and academia. The program is differentiated from traditional programs by catering to non-traditional students through specific pedagogies such as incorporating required mathematics competencies into Data Analytics courses, using specific pedagogies proven to work with the non-traditional population, as well as removing constraints by offering evening courses, easing registration obstacles, etc. The paper suggests a proposed curriculum, as well as discussing the rationale behind each differentiated option.

Download this article: ISEDJ - V18 N3 Page 18.pdf

Recommended Citation: Mew, L., (2020). Developing an Undergraduate Data Analytics Program for Non-Traditional Students. Information Systems Education Journal18(3) pp 18-27. ISSN : ISSN: 1545-679X. A preliminary version appears in The Proceedings of EDSIGCON 2019