Fostering Undergraduate Data Science


GÖKALP YAVUZ F., Ward M. D.

AMERICAN STATISTICIAN, vol.74, no.1, pp.8-16, 2020 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 74 Issue: 1
  • Publication Date: 2020
  • Doi Number: 10.1080/00031305.2017.1407360
  • Journal Name: AMERICAN STATISTICIAN
  • Journal Indexes: Science Citation Index Expanded, Scopus, Academic Search Premier, ABI/INFORM, Business Source Elite, Business Source Premier, CAB Abstracts, EBSCO Education Source, EconLit, Education Abstracts, Public Affairs Index, zbMATH, DIALNET
  • Page Numbers: pp.8-16
  • Keywords: Computation, Learning, Mentoring, Statistical projects, Teamwork

Abstract

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.