Bridging Data Science Programming with Advanced Formal Coursework

Wesley A. Brashear, Zhenhua He, Richard Lawrence, Dhruva K. Chakravorty, Tatevik Sekhposyan, Margaret L. Carpenter, and Honggao Liu

Volume 13, Issue 2 (December 2022), pp. 2–7

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  author={Wesley A. Brashear and Zhenhua He and Richard Lawrence and Dhruva K. Chakravorty and Tatevik Sekhposyan and Margaret L. Carpenter and Honggao Liu},
  title={Bridging Data Science Programming with Advanced Formal Coursework},
  journal={The Journal of Computational Science Education},
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In order to fulfill the needs of an evolving job market, formal academic programs are continuously expanding computational training in traditional discipline-specific courses. We developed an informal, twelve contact-hour course tailored for economics students entering a computationally rigorous graduate-level course to help mitigate disparities in computing knowledge between students and prepare them for more advanced instruction within the formal setting. The course was developed to teach the R programming language to students without assuming any prior knowledge or experience in programming or the R environment. In order to allow for ease of implementation across various training approaches, the course was modularized with each section containing distinct topics and learning objectives. These modules can be easily developed as independent lessons so that discipline-specific needs can be addressed through inclusion or exclusion of certain topics. This implementation used the R package 'learnr' to develop the course, which rendered a highly extensible and portable interactive Shiny document that can be deployed on any system on which RStudio is installed. The course is offered as a series of interactive sessions during which students are led through the Shiny notebook by an instructor. Owing to its structure, it can be offered as an asynchronous web-based set of tutorials as well.