The aim of this module is to provide the student with and understanding of the role data mining and data quality techniques play in our lives. The students will develop a basic toolbox allowing them to use methods for learning and evaluation information. This will be paired with a consideration of the ethical implications surrounding gathering and using information in an automated manner.

1. Execute and evaluate various techniques in knowledge discovery and data mining
2. Analyse and critically evaluate methods for assuring quality of information
3. Appraise the ethical implications and societal risks associated with data mining and data quality assurance

Modules will be delivered through blended learning. You will be guided through learning activities appropriate to your module, which may include:

• on-line resources that you work through at your own pace (e.g. videos, web resources, e-books, quizzes),
• on-line interactive sessions to work with other students and staff (e.g. discussions, live streaming of presentations, live-coding, team meetings)
• face to face small group sessions (e.g. help classes, feedback sessions)

Please refer to the learning outcomes.

A blend of assessment types which may include coursework and portfolio assessments, class tests, and/or formal examinations.

Students will be provided with reassessment opportunities in line with University regulations.

Similarity measures

Knowledge discovery process

Data mining

Classification

Clustering

Association rule learning

Data quality dimensions, activities and methodologies

Ethical considerations concerning gathering and using information