Data-driven learning (DDL) is an instructional approach that uses corpora and corpus analytic tools to learn about the target language. This entry introduces the core techniques of DDL within the context of Computer-Assisted Language Learning (CALL), particularly targeting learners with little DDL experience. This entry introduces the core techniques of data-driven learning (DDL), its relevant theories and characteristics, commonly used corpora and software, and offers practical examples to illustrate how this approach can be implemented with learners. Through a brief discussion of a range of topics that include hands-on and hands-off DDL, teacher-led and student-led DDL activities, and the selection of general or self-built corpora, we show that the DDL approach can support the development of language skills in both the classroom environment and in autonomous language learning contexts. We show that the DDL approach can both respond to learners’ momentary language-related queries and contribute to longer-term language learning and teaching objectives. By possessing even rudimentary DDL skills, language learners and teachers can independently access and process ever-expanding resources for language analysis.