Teaching Practical Data Science

Oregon Convention Center 

While there has been considerable work on guiding educators on how to structure a course in data science for imparting technical knowledge (e.g. Hicks and Irizarry (2018)), we argue, based on employer feedback and industry relations, that a larger part of the curriculum needs to be devoted to problem formulation, deployment, solution design, model monitoring and communication of results. Emphasising these practical aspects imposes new requirements on the instructor and the coordinating department. An example of the demand on the instructors is the breadth of knowledge they are required to know. The department, on the other hand, needs a steady stream of case studies for students to work on; this is exacerbated by increasing class sizes. In this talk we present our observations and thoughts on these challenges, based on our experience of teaching these topics over 4 semesters to approximately more than 400 students (and growing).

1. Hicks, Stephanie C., and Rafael A. Irizarry. "A guide to teaching data science." The American Statistician 72, no. 4 (2018): 382

data science

practice

end-to-end

teaching

curriculum

syllabus 

Section on Statistics and Data Science Education