A Data Science Course for Undergraduates: Thinking with Data

TL;DR

This paper presents a comprehensive undergraduate data science course designed to equip students with practical skills and a structural thinking framework for handling complex, real-world data across various stages of analysis.

Contribution

It introduces a novel undergraduate course that covers the full data analysis process, emphasizing practical skills and a conceptual framework for data science in a liberal arts setting.

Findings

Students gain skills in data acquisition, management, analysis, and visualization.

The course prepares students to think structurally about complex data problems.

Students can communicate findings effectively through multiple formats.

Abstract

Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings. These data tend to be non-traditional, in the sense that they are often live, large, complex, and/or messy. A first course in statistics at the undergraduate level typically introduces students with a variety of techniques to analyze small, neat, and clean data sets. However, whether they pursue more formal training in statistics or not, many of these students will end up working with data that is considerably more complex, and will need facility with statistical computing techniques. More importantly, these students require a framework for thinking structurally about data. We describe an undergraduate course in a liberal arts environment that provides students with the tools necessary to apply data science. The course emphasizes modern, practical, and useful skills that cover the full data analysis spectrum, from asking an interesting question to acquiring, managing, manipulating, processing, querying, analyzing, and visualizing data, as well communicating findings in written, graphical, and oral forms.