Fostering Undergraduate Data Science

Date

2020-01-01

Author

Gökalp Yavuz, Fulya
Ward, Mark Daniel

Metadata

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Data Science is one of the newest interdisciplinary areas. It is transforming our lives unexpectedly fast. This transformation is also happening in our learning styles and practicing habits. We advocate an approach to data science training that uses several types of computational tools, including R, bash, awk, regular expressions, SQL, and XPath, often used in tandem. We discuss ways for undergraduate mentees to learn about data science topics, at an early point in their training. We give some intuition for researchers, professors, and practitioners about how to effectively embed real-life examples into data science learning environments. As a result, we have a unified program built on a foundation of team-oriented, data-driven projects.

Subject Keywords

Statistics, Probability and Uncertainty, Statistics and Probability, General Mathematics

URI

https://hdl.handle.net/11511/43893

Collections

Department of Statistics, Article