Slides and demo materials for the "Teaching Data Science and Statistical Computation to Undergraduates" talk at USCOTS 2017
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README.md

data-science-uscots-2017

Slides and demo materials for the "Teaching Data Science and Statistical Computation to Undergraduates" pleanary talk and breakout session at USCOTS 2017

  • Talk: Saturday, May 20th, 8:30am - 9:30am (a recording will be available after the conference ends, link will be added here)
  • Breakout session: Saturday, May 20th, 11:00 am – 12:15 pm

Find out more about the conference at https://www.causeweb.org/cause/uscots/uscots17/.

Abstract

Talk:

What draws students to statistics? For some, the answer is mathematics, and for those a course in probability theory might be an attractive entry point. For others, their first exposure to statistics might be an applied introductory statistics course that focuses on methodology. This talk presents an alternative focus for a gateway to statistics: an introductory data science course focusing on data wrangling, exploratory data analysis, data visualization, and effective communication and approaching statistics from a model-based, instead of an inference-based, perspective. A heavy emphasis is placed on best practices for statistical computation, such as reproducibility and collaborative computing through literate programming and version control. I will discuss specific details of this course and how it fits into a modern undergraduate statistics curriculum as well as the success of the course in recruiting students to a statistics major.

Breakout session:

We will discuss ideas and issues that emerge in the keynote presentation about developing and teaching an introductory data science course focusing on data wrangling, exploratory data analysis, data visualization, and effective communication.