Introduction to Data Mining
Subject code: MA5810:03
This subject will provide students with a range of widely used algorithms and techniques to automatically extract patterns from data. Students will learn a range of classic yet powerful and often applied techniques for the most common descriptive and predictive tasks in data mining, including clustering, outlier detection, and classification. The algorithms and techniques will be studied both at the conceptual as well as at the practical levels. Software packages will be adopted for hands-on data mining in real data sets.
Software platform: 
Learning outcomes
- Explain what data mining is about and exemplify the most common tasks and types of data mining problems
- Describe, choose, and apply classic unsupervised data mining methods for descriptive analytics tasks, such as clustering and outlier detection
- Describe, choose, and apply classic unsupervised and supervised techniques for dimensionality reduction
- Describe, choose, and apply classic supervised data mining methods for pattern classification
Assessment
Assessment for this course will occur at various times across the seven-week study period. Tasks may include online quizzes, discussion board activity, portfolio development, case studies, reflection, literature reviews presentations and reports.Feedback will be provided to you throughout the study period as well as a final grade at the conclusion of the study period. Click here to find out more about this subject's assessments.
This is one of the interdisciplinary subjects studied in the online Master of Data Science.
Please note, course structure and content are subject to change. For information on all course subjects download the course guide.
