Computing in the Statistics and Data Science Curriculum

Tuesday, January 26th, 20214:00 pm – 5:00 pm ET

Presented by: Mine Çetinkaya-Rundel (University of Edinburgh/RStudio) & Alex Reinhart (Carnegie Mellon University)


The Journal of Statistics and Data Science Education special issue on “Computing in the Statistics and Data Science Curriculum” features a set of papers that provide a mosaic of curricular innovations and approaches that embrace computing. The collected papers (1) suggest creative structures to integrate computing, (2) describe novel data science skills and habits, and (3) propose ways to teach computational thinking.

In this webinar, we've invited two authors of papers in the special issue to talk about their work and to answer questions originally posed by Nolan and Temple Lang in their 2010 TAS paper "Computing in the Statistics Curriculum":

  1. When they graduate, what ought our students be able to do computationally, and are we preparing them adequately in this regard?
  2. Do we provide students the essential skills needed to engage in statistical problem solving and keep abreast of new technologies as they evolve?
  3. Do our students build the confidence needed to overcome computational challenges to, for example, reliably design and run a synthetic experiment or carry out a comprehensive data analysis?
  4. Overall, are we doing a good job preparing students who are ready to engage in and succeed at statistical inquiry?