  The world is full of text data, yet text analytics has not traditionally
played a large part in statistics education. We consider four different ways to
provide students with opportunities to explore whether email messages are
unwanted correspondence (spam). Text from subject lines are used to identify
features that can be used in classification. The approaches include use of a
Model Eliciting Activity, exploration with CODAP, modeling with a specially
designed Shiny app, and coding more sophisticated analyses using R. The
approaches vary in their use of technology and code but all share the common
goal of using data to make better decisions and assessment of the accuracy of
those decisions.
