Data mining is the computational process of discovering patterns from large data sets. This unit discusses concepts and techniques of data warehousing and mining. Data mining is one of the most advanced tools used by IT industries. The topics covered include introduction to data warehousing, data pre-processing and foundational data mining techniques such as supervised learning including regression and classification, and unsupervised learning such as clustering and association rules. Students are introduced to perform data mining tasks with neural networks, as well as exposed to open-source data mining software.

Unit details

Location:
Study level:
Postgraduate
Credit points:
12
Unit code:
NIT6160

Learning Outcomes

On successful completion of this unit, students will be able to:
  1. Critically analyse the application of data warehousing concepts;  
  2. Critically evaluate data quality to advocate application of data pre-processing techniques;  
  3. Devise implementation solutions that involve supervised learning including regression and classification on real world data sets;  
  4. Experiment and evaluate unsupervised learning solutions including clustering and association rules mining on large datasets; and  
  5. Critically reflect on advantages and disadvantages of particular data mining solutions to solve real life problems, and evaluate their usefulness and useability.  

Assessment

Assessment type Description Grade
Laboratory Work Practical labs 20%
Test Open-book test 40%
Project Final Project 40%

Where to next?

As part of a course

This unit is studied as part of the following courses. Refer to the course page for information on how to apply for the course.

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