HKU Moodle

This course provides a broad overview of the key concepts, skills, technologies and applications in data science, with an emphasis on educational applications of data science. Learners will explore principles, methods and application cases in data pre-processing and storage, inferential and predictive analytics, supervised and unsupervised machine learning, association rule mining, text analytics, network analysis, data visualization, as well as data ethics and privacy. Example cases will be discussed to illustrate how learning analytics needs to be connected to the targeted learning outcomes and the pedagogical design considerations to provide valuable feedback to learners and teachers to improve teaching and learning.

Besides attending lectures and participating in discussions, students will conduct labs and tutorials to gain hands- on experience on using industry-standard data mining and/or learning analytics packages to solve practical data- driven problems. This also builds up students’ abilities in the group project, the capstone of this module, where students will collectively design data analytic applications to solve real-world problems.