Salt Lake City, Utah
June 23, 2018
June 23, 2018
July 27, 2018
Computing & Information Technology: Curriculum and Assessment
Computing and Information Technology
We discuss opportunities and challenges encountered in developing new undergraduate degree programs that are inherently cross-disciplinary and require institutional and instructional support from different departments and colleges. We have recently been involved in early stages of curriculum development for an undergraduate BA/BS Data Analytics program at a prominent research university in the Pacific Northwest, involving faculty and resources from Computer Science, Mathematics & Statistics, College of Business, and other units at the University. The development of such a new degree program required developing entirely new courses and their syllabi, identifying faculty across the existing academic units suitable and available to teach those new courses, as well as identifying key existing courses across Computer Science, Mathematics, Statistics and Business that should be incorporated into the new degree program. One challenge there, was to ensure providing the solid foundations and a right balance to the future Data Analytics graduates, without making their BA/BS degree program require substantially more credit hours than the existing undergraduate programs in e.g. Statistics, Mathematics or the Data Science "track" in Computer Science.
We share some insights from this curriculum development process, primarily from a computer science perspective. Some issues that we have encountered include the following: what are the most important skills that the future BA or BS graduates in Data Analytics should acquire from computational and computer science standpoints? Is requiring those students to take the existing CS courses on e.g. Data Mining, Machine Learning or Database Systems appropriate, or should new courses (not shared with existing CS curriculum Data Science or similar "tracks") be developed? What is the right balance between training in computational (and mathematical) foundations, vs. various application domains, such as in business or life sciences? One of the key early lessons we have learned: effective, respectful and cooperative communication across the traditional disciplinary boundaries is absolutely crucial for intrinsically cross-disciplinary higher-education undertakings such as developing a new data analytics degree program to be successful.
Tosic, P. T., & Beeston, J. (2018, June), Designing Undergraduate Data Science Curricula: A Computer Science Perspective Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--30283
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