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MA5810 - Introduction to Data Mining

Credit points: 3
Year: 2019
Student Contribution Band: Band 2
Administered by: College of Science and Engineering

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.

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.
Prerequisites: MA5820 AND MA5800

Availabilities

External, Study Period 84
Census Date 18-Jul-2019
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 65 hours - Online resources including readings, screencasts, embedded quizzing
  • assessment and self-directed study
Method of Delivery: WWW - LearnJCU
Assessment: quizzes or tests (20%); assignments (60%); computational laboratories/log book (20%).

JCU Online, External, Study Period 84
Census Date 18-Jul-2019
Coordinator: Professor Ronald White
Lecturer: Ms Penny Harris.
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 65 hours - Online resources including readings, screencasts, embedded quizzing
  • assessment and self-directed study
Method of Delivery: Online - JCU
Assessment: quizzes or tests (20%); assignments (60%); computational laboratories/log book (20%).

Note: Minor variations might occur due to the continuous Subject quality improvement process, and in case of minor variation(s) in assessment details, the Subject Outline represents the latest official information.