coursemapLoading course
School of Computing · ANU College of Systems and Society
Real-world data are commonly messy, distributed, and heterogeneous. This course introduces core concepts of data cleaning and standardisation, and data integration, that are aimed at converting and mapping raw data into other formats that allow more efficient and convenient use and analysis of data. The courses also discusses data quality, management, and storage issues as relevant to data analytics.
Written and practical assignments
Learning outcomes 1, 2, 3, 4, 5
Final examination
Learning outcomes 1, 2, 3, 4, 5
The workload for the course is around 130 hours, including reading, the viewing of online course material, participation in face-to-face lectures, practical labs and tutorials, and preparation for assessments.
None Preliminary Reading - Data matching - Concepts and techniques for record linkage, entity resolution and duplicate detection (Peter Christen, Springer, 2012). This book is a required text for major parts of the course. There are several copies available in the ANU library. - Data mining: Concepts and techniques, 3rd edition (Jiawei Han, Micheline Kamber and Jian Pei, Morgan Kaufmann, 2011) Note: This is also the text book for the data mining course (COM3425 and COMPP8410). - Data mining with Rattle and R is a useful book if you plan to use Rattle in this course as well as the Data Mining course (COMP3425 and COMP8410).