Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2023

Abstract

Unprecedented demand for data science professionals in the industry has led to many educational institutions launching new data science courses. It is however imperative that students of data science programmes learn through execution of real-world, authentic projects on top of acquiring foundational knowledge on the basics of data science. In the process of working on authentic, real-world projects, students not only create new knowledge but also learn to solve open, sophisticated, and ill-structured problems in an inter-disciplinary fashion. In this paper, we detailed our approach to design a data science curriculum premised on learners solving authentic data science problems sourced from an overseas project sponsor. The course consists of a local component in which students acquire requisite knowledge on data analytics through classroom-based learning and an overseas component where students were on-site with the overseas project sponsor. To evaluate the design of our course curriculum and uncover its potential benefits, we surveyed the project sponsor representatives and tasked our students with the submission of a personal reflection. Quantitative and qualitative analysis of the survey and reflections respectively revealed the sponsors’ high satisfaction with the students’ output and the students’ perception that the course has enriched their learning. The results also indicated that the students attributed teamwork, communications, mentoring and data processing skills as key factors integral to successful project outcomes. In all, we have developed an experiential, project based and applied data science program in an overseas context offered to students from different disciplines which yields promising student learning outcomes.

Keywords

Curriculum design, Experiential learning, Analytics, Data science, Machine learning, Multi-disciplinary

Discipline

Artificial Intelligence and Robotics | Curriculum and Instruction | Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Optimization

Publication

2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE): Auckland, November 27 - December 1: Proceedings

First Page

1

Last Page

7

ISBN

9781665453318

Identifier

10.1109/TALE56641.2023.10398363

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TALE56641.2023.10398363

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