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Abstract:
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Innovative training methods are essential for data science. We advocate an approach to data science training for undergraduate students that utilizes several types of computational tools, including R, bash, SQL, and XPath, often used in tandem. We also advocate introducing students to research opportunities early in their undergraduate experiences. We believe that data science training should be an interdisciplinary experience for such students. Students benefit from blending their research and coursework, as well as having support from peers and faculty research mentors. Active learning environments and living-learning communities both lend themselves well to unifying such data science training opportunities. We share lessons learned from two NSF training grants for undergraduates. We give some insights for researchers, professors, and practitioners, about how to effectively embed real-life examples into data science learning environments. We advocate building on a foundation of team-oriented, data-driven projects, courses, seminars, problem solving sessions, and opportunities for students to develop their own projects and communicate the results.
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