Deb Nolan (University of California – Berkeley)
Schedule
Friday, May 19, 1:45-2:45pm
Abstract
Statistics teaching typically has students work with data that are ready for them to apply a particular method to carry out a statistical analysis. This approach makes a lot of sense for most of our courses, but should not be our students’ only encounter with data. Working with raw data can lead to better statistical thinking skills and build confidence in our students that they are capable of handling new problems and data in the future. However, we need to expose the computational thinking involved as we wrangle with data and avoid treating this process as simply an ad hoc messy task. Furthermore, when we infuse statistical thinking earlier in the data-analysis lifecycle, students gain an alternative and important opportunity to learn. In this talk, we describe an integrated approach to teaching that incorporates computational and statistical thinking skills throughout the fuller data-analysis lifecycle, from data acquisition and cleaning to data organization and analysis to communicating results.