2024 ASEE Annual Conference & Exposition

This paper describes an interdisciplinary data analytics professional master’s program which comprises courses from the disciplines of computer science, statistics, and design. The online curriculum structure specifically addresses the needs of working professionals with little to no prior data science, computing, or math background. Courses use both synchronous and asynchronous delivery methods to maximize learner flexibility while providing opportunities to engage in real time with instructors and peers. All courses emphasize projects to provide opportunities for learners to apply courses concepts to real-world problems. A terminal 2-semester capstone course incorporates all three disciplines into a final culminating team project. This paper will focus on the conceptualization of the computer science (CS) portion of the curriculum. As an applied master’s program, much of the CS curriculum takes inspiration from industry frameworks such as CRISP-DM and Agile project management to contextualize concepts. The curriculum incorporates design and design thinking concepts to emphasize creative problem-solving skills and the importance of data storytelling.
There is a need for educators to understand how to develop a curriculum for working professionals which introduces novice programmers to 1) core data and computational concepts; 2) tools and techniques; 3) data-driven problem-solving workflows; and 4) data storytelling. This paper presents these four “swim lanes” to define a framework for describing a cohesive interdisciplinary curricular experience for an applied master’s program.
Through reflection, the authors conclude that learners initially struggle with new concepts, but with sufficient support, they successfully learn and apply data science and computer science concepts in both didactic and experiential settings. Students appreciate the need to successfully communicate with data and be effective data storytellers but will often feel frustrated that data storytelling skills are not “real data science.” An analysis of LinkedIn profiles indicates that over 60% of graduated learners secured new employment in data careers since starting the program. To build on this success, further curriculum development should more explicitly connect fundamental data science concepts and broader concepts such as creative problem-solving and data storytelling.