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MA5405 - Data Mining

Credit points: 3
Year: 2023
Student Contribution Band: Band 1
Administered by: College of Science and Engineering

Available to postgraduate science students.

Recent advances in technology makes it possible to collect, store and analyse very large data sets. Consequently, the contemporary scientist must be skilled in extracting important information embedded in large and complex data sets if they are to offer advances in knowledge to industry, business, research and societies of the 21st century. Moreover, employers are increasingly demanding that graduates can make important discoveries by interrogating large data sets. This subject will provide the bridge between mathematical theory and applied computing methods via the R programming language to give students a strong grounding in statistical learning methods for analysing Big Data sets. A range of supervised and unsupervised learning methods will be covered.

Learning Outcomes

  • translate between mathematical, visual and conceptual characterisations of statistical learning methods suitable for Big Data;
  • evaluate large and complex data sets using appropriate data mining techniques;
  • design, implement and validate supervised and unsupervised machine learning systems;
  • implement statistical models in the R computing environment;
  • learn techniques for coping with the analysis of large data sets.

Subject Assessment

  • Written > Examination - In class - (40%) - Individual
  • Written > Test/Quiz 1 - (10%) - Individual
  • Capstone assignment - (50%) - Individual.
Students must have a good understanding of STATISTICS which includes knowledge of basic probability, hypothesis testing, law of large numberes, central limit theorum and ability to use R for data analysis (or have done the JCU R Bootcamp). SC5202 or SC2202 or SC2209 or will have acquired equivalent knowledge through industry experience.
Prerequisites: MA2405 OR MA2000 OR SC2202 OR SC2209 OR SC5202


Townsville, Study Period 2, Internal
Census Date 24-Aug-2023
Coordinator: Dr Carla Ewels
Lecturers: Dr Carla Ewels, Professor Yvette Everingham.

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.