A Blueprint to Design Curriculum and Pedagogy for Introductory Data Science

TL;DR

This paper offers a flexible, experience-based blueprint for developing modern introductory data science curricula that incorporate current technologies and address common teaching challenges.

Contribution

It provides a practical, adaptable curriculum blueprint based on real teaching experience, including discussion of challenges and reproducible teaching materials.

Findings

Identifies key challenges in teaching modern data science courses.

Proposes a learning model for student understanding of data science.

Provides reproducible curriculum materials for instructors.

Abstract

As the demand for jobs in data science increases, so does the demand for universities to develop and facilitate modernized data science curricula to train students for these positions. Yet, the development of these courses remains challenging, especially at the introductory level. To help instructors to meet this demand, we present a flexible blueprint that supports the development of a modernized introductory data science curriculum. This blueprint is narrated through the lens and experience in teaching the introductory data science course at \university{}. This is a large course that serves both STEM and non-STEM majors and includes the incorporation and facilitation of technologies such as R, RStudio, Quarto, Git, and GitHub. We identify and provide discussion around common challenges in teaching a modernized introductory data science course, detail a learning model for students to grow their understanding of data science concepts, and provide reproducible materials to help empower teachers to adopt and adapt such curriculum at their universities.