Success Factors for Using Case Method in Teaching Applied Data Science Education

Abstract

In a world where everything involves data, an application of it became essential to the decision-making process. The Case Method approach is necessary for Data Science education to expose students to real scenarios that challenge them to develop the appropriate skills to deal with practical problems by providing solutions for different activities. Data science combines multiple fields like statistics, scientific methods, and data analysis to extract value from data, being an umbrella term used for multiple industries, such as data analytics, data mining, machine learning, big data, business intelligence, and predictive analytics. This paper gives an overview of success factors for using the Case Method in teaching Applied Data Science education. Showing that close analysis provides a deeper understanding of implications, connects theory to practice, and classes unfold without a detailed script when successful instructors simultaneously manage content and process. This synthesis of current research can be used by Applied Data Science educators to more effectively plan the use of the Case Method as one possible teaching method.