Document Type
Article
Publication Date
12-29-2015
Publication Title
The American Statistician
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 nontraditional, 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 to a variety of techniques to analyze small, neat, and clean datasets. However, whether they pursue more formal training in statistics or not, many of these students will end up working with data that are 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. Supplementary materials for this article are available online.
Keywords
Computational statistics, Data science, Data visualization, Data wrangling, Machine learning, Statistical computing, Undergraduate curriculum
Volume
69
Issue
4
First Page
334
Last Page
342
DOI
dx.doi.org/10.1080/00031305.2015.1081105
Rights
Licensed to Smith College and distributed CC-BY under the Smith College Faculty Open Access Policy.
Recommended Citation
Baumer, Benjamin, "A Data Science Course for Undergraduates: Thinking with Data" (2015). Mathematics Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/mth_facpubs/25
Comments
Peer reviewed accepted manuscript.