An Introductory R Course for Statistics Majors (306337)*Laura Taylor, Elon University
Keywords: statistics education, data science, R, course content
This poster discusses a course, Statistical Computing for Simulation and Theory, developed at an undergraduate, liberal arts university to teach the R language. While primarily developed for statistics majors and minors, the course has a pre-requisite of a 200-level introductory statistics course and therefore is also populated by students interested in learning R. The course explores statistical theory (such as the Central Limit Theorem, impacts of assumption violations on analyses, and the power of a test) through the lens of student-coded simulations using R. The poster covers course content, pedagogical strategies, and assessments used in this course. One assessment highlighted in the poster is the final course project where students illustrate their statistical conceptual knowledge and ability to communicate statistics through a Shiny app and blog post. Changes between the first and second offerings of the course are also presented. This interactive poster invites viewers to share their ideas about what content this type of course should cover and how this course might differ with a focus on data science.