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MA3405 - Statistical Data Mining for Big Data

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

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 statistical modelling techniques;
  • design, implement and validate supervised and unsupervised machine learning systems;
  • implement statistical models in the R computing environment;
  • learn techniques for coping with large data sets.
Prerequisites: MA2405 OR MA2000

Availabilities

Townsville, Internal, Study Period 2
Census Date 29-Aug-2019
Coordinator: Assoc. Professor Yvette Everingham
Lecturers: Dr Carla Ewels, Dr Neil Fraser, Assoc. Professor Yvette Everingham.
Workload expectations:

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

  • 26 hours on-campus > Lectures
  • 13 hours on-campus > Practicals
  • assessment and self-directed study
Assessment: other exams (50%); assignments (50%).

Cairns, Internal, Study Period 2
Census Date 29-Aug-2019
Coordinator: Dr Bronson Philippa
Lecturers: Dr Bronson Philippa, Dr Neil Fraser, Dr Sourav Das.
Workload expectations:

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

  • 26 hours on-campus > Lectures
  • 13 hours on-campus > Practicals
  • assessment and self-directed study
Assessment: other exams (50%); assignments (50%).

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.