Data Science Curricula on the Viewpoint of Data Science Process

Abstract

　The "AI Strategy 2019" calls for all university and technical college students, regardless of their faculties or departments, to learn mathematics, data science, and AI at an elementary level. In order to design an educational program for such students of large masses and diversity, it is necessary to design not only model curricula, but also educational programs from a higher perspective. In this paper, we focus on data science processes. By presenting one data science process, both educators and learners can design the content to be learned by clarifying the purpose of data science, rather than relying on the structure of the academic discipline that forms the basis of data science. In particular, it is possible to understand that "data acquisition, management, and processing" is as important as "data analysis and inference". Finally, we compare data science processes with software development models, and discuss the possibility of a new data science process.