Learning Analytics (LA) is an emerging research field that harnesses the power of data modelling, data mining and visualization to enhance the understanding of teaching and learning as well as supporting the personalization of education. Typical LA applications include dashboards displaying course progress, intelligence reports tracking the use of educational resources, and systems that predict students' academic performance and identify struggling students. In this article, we review the major elements of LA applications, including typical workflows, data types, and approaches to analytics. In addition to the basic reporting tools available in Learning Management Systems (LMSs), the article provides insights into how Machine Learning (ML) can detect the students at risk of failing. Finally, six educational applications in which data analytics helps improve course delivery are discussed.