Developing and Implementing an Undergraduate Data Analytics Program for Non-Traditional Students.
This paper discusses Implementation of 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 implement 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, discusses the rationale behind each differentiated option, and explains how the program is being implemented.
Copyright © 2020, Information Systems Education Journal (ISEDJ).
Mew, Lionel. “Developing and Implementing an Undergraduate Data Analytics Program for Non-Traditional Students.” Information Systems Education Journal 18, no. 3 (June 2020): 18. https://isedj.org/2020-18/n3/ISEDJv18n3p18.html.