Data-driven learning (DDL) is an inductive approach to language learning in which students study examples of authentic language and use them to find patterns of language use. This inductive approach to learning has the advantages of being learner-centered, encouraging hypothesis testing and learner autonomy, and helping develop learning skills. The approach has grown out of corpus linguistics, and it has been used to help students learn grammar and vocabulary usage. It is also possible to use it for teaching speech acts. In this paper, we contrast inductive and deductive learning and discuss an example of an exercise using data-driven learning to learn about the expressions and strategies used in apologies. [For full proceedings, see ED565044.]