A fresh look at introductory data science

TL;DR

This paper discusses the design, implementation, and assessment of an introductory data science course at Duke University aimed at diverse students, emphasizing pedagogical strategies, content, and open-source resources.

Contribution

It provides a detailed case study of a comprehensive, accessible data science course with open materials, addressing educational challenges in training diverse student populations.

Findings

Course successfully attracts a wide range of students.

Open-source materials facilitate reproducibility and adaptation.

Pedagogical design effectively addresses course challenges.

Abstract

The proliferation of vast quantities of available datasets that are large and complex in nature has challenged universities to keep up with the demand for graduates trained in both the statistical and the computational set of skills required to effectively plan, acquire, manage, analyze, and communicate the findings of such data. To keep up with this demand, attracting students early on to data science as well as providing them a solid foray into the field becomes increasingly important. We present a case study of an introductory undergraduate course in data science that is designed to address these needs. Offered at Duke University, this course has no pre-requisites and serves a wide audience of aspiring statistics and data science majors as well as humanities, social sciences, and natural sciences students. We discuss the unique set of challenges posed by offering such a course and in light of these challenges, we present a detailed discussion into the pedagogical design elements, content, structure, computational infrastructure, and the assessment methodology of the course. We also offer a repository containing all teaching materials that are open-source, along with supplemental materials and the R code for reproducing the figures found in the paper.