Challenges of Applied Data Science Education: Contribution of Case Method Teaching

Valentina Chkoniya
University of Aveiro

Abstract

Data science is a new profession emerging along with the exponential growth in the era of digital overexposure. A data scientist provides support to the decision-making process by looking at past and current data. Application of it is an essential skillset in a world of uncertainty where everything from communication to transport, industry to commerce, involves data. Harvard Business School Professor C. Roland Christensen was the world's leading authority on case method teaching, which he described as "the art of managing uncertainty". This paper gives an overview of possible contributions of case method teaching to respond to challenges of applied data science education. Showing that unlike lectures, applied data science education cannot happen only in passive reception of knowledge classroom isolation, it requires a deeper understanding of implications in real cases. Through their close analysis, the case method connects theory to practice, and classes unfold without a detailed script when successful instructors simultaneously manage content and process. Efficiently integrating consumer behavior data into marketing strategies can help companies improve their approach towards attracting and winning the diverse and dynamic consumer segments and retaining them. This synthesis of current research can be useful to teaches and the student community by providing evidence about the contribution of case learning methodology in applied data science education.

Presentation