Introduction to Data Mining

Subject code: MA5810:03

This subject will provide you with a range of widely used algorithms and techniques to automatically extract patterns from data. You’ll 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.

Learning outcomes

Students who successfully complete this subject will be able to:

  • 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.

Please note, unit structure and content are subject to change. Contact your JCU Online student advisor on 1300 535 919 for more information based on your particular circumstances.

 

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