Abstract:

This article contributes to the existing body of literature in a unique way by proposing innovative ways to teach Decision Science so that students are provided the cognizance, scaffolding, and context needed for success in their later study of Data Science and Analytics. It prepares undergraduate business students for the on-ramp required of algorithms and Machine Learning.

The fields of Data Science and Analytics are growing in importance yet businesses experience difficulty filling job positions due to workers without the necessary analytic skills (Russom, 2011). In addition, academic institutions struggle to prepare students for business careers in these fields. A Data Science degree for instance has a specific focus and learning outcomes emphasizing the skills of mathematics and statistics but lacking explanations of how these tools could be applied in real-world business settings (Discover Data Science, 2019). Debortoli, M¨uller, and vom Brocke (2014) found business acumen to be as important as technical skills for BI and big data initiatives.

Decision Science is an interdisciplinary mathematical science sometimes referred to as Operations Research (O.R.). O.R. is a sub-field of Applied Mathematics alongside other areas such as Data Science and Machine Learning. O.R. relies heavily on algorithms, mathematics, and statistics. Data Science overlaps with statistics, computer science, data mining, operations research and business intelligence. Numerous undergraduate business programs offer a Decision Science course, but it is often taught in a traditional way. Teaching Decision Science has always been challenging due to many factors, including students’ fear of statistics and quantitative-method courses. Innovative teaching strategies may help to remedy the challenge (Boaler, 2016). For instance, an experiential approach to education, incorporating project-based learning methods, as well as active learning pedagogic techniques could augment student learning outcomes and engagement.

This article extracts and synthesizes insights from numerous academic papers and recommends a teaching approach and instructor’s role that includes innovations in teaching pedagogy, and alternative assessment methods. It also suggests using different course assignments and computer lab projects designed to enhance appreciation of business and data analytics. The overall effect is that the approach sometimes contrasts the model of the traditional educational environment with that of a professional learning community.

Some researchers have outlined how a graduate level big data analytics course was delivered to students at a university (Asamoah, Sharda, Hassan Zadeh, & Kalgotra, 2017). Other research demonstrates that business undergraduate students can benefit from experiential project-based learning (Yazici, 2020). Some authors present the skills sought by employers for an entry-level analytics position (Stanton & D’Auria Stanton, 2020). Researchers identify desirable student characteristics, such as communication skills and business acumen (Paul & MacDonald, 2020). Notwithstanding these papers, there appears to be a gap in literature outlining how an undergraduate business program could use a Decision Science course to help prepare students for the later study of Data Science and Analytics. This is the article’s contribution as it aims to assist business instructors in preparing undergraduate students for the study of Data Science and Analytics.

Keywords:

Decision Science, Data Science, Analytics, Business, Applied Mathematics, Project-based Learning, Active Learning, Pedagogy, Professional Learning Community.