Effects of Prior Academic Experience in Introductory Level Data Science Course

Abstract

Data Science is an in-demand skill in the job market. To meet the demand, universities are offering data science courses or programs. These courses/programs not only give students data science skills, but also awareness of the field, increasing the likelihood of their opting for data science as a career. In this work we look at how the students' prior academic experience affects the outcome of an introductory data science class that requires no pre-requisites. We conducted the study in an undergraduate introductory data science class (IS 296) at the University of Maryland, Baltimore County (UMBC). The course was adapted from University of California Berkeley's Data 8 course. A pre and post survey was conducted to measure four factors in light of Social Cognitive Career Theory (SCCT): self-efficacy, identity, motivation and belonging uncertainty of students as a data scientist before and after taking the class. Our results show that although the course was designed requiring no pre-requisites, students with no prior programming experience and no statistics experience showed decrease in the four factors as compared to students with some prior experience.