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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, 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
Oral presentation and report
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 Other Information Professional Skills Mapping: Mapping of Learning Outcomes to Assessment and Professional Competencies
None Preliminary Reading Data Matching - Concepts and Techniques fro Record Linkage, Entity Resolution and Duplicate Detection, Peter Christen, Springer, 2012 For more information see: http://users.cecs.anu.edu.au/~christen/data-matching-book-2012.html