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Advanced Data Mining
Postgraduate | SWI-STA80005 | 2019
Course information for 2019 intake
Mine data and inspect the underlying structure of market research and social science. Navigate dimensional analysis mapping, segmentation and preference techniques. Map suitable classification methods and report data mining results.
- Study method
- 100% online
- Assessments
- Subject may require attendance
- Entry requirements
- Part of a degree
- Duration
- -
FEE-HELP available
Advanced Data Mining
About this subject
Students who successfully complete this subject will be able to:
- Formulate an understanding of the basic theory and principles of data mining
- Assess prediction and classification methods such as regression, neural networks and decision trees, understanding how to choose between these methods
- Evaluate unsupervised data mining methods and determine when these methods should be combined with supervised methods
- Design ontologies based on text and creative visualisations of textual relationships
- Report on the results of specific data mining projects.
- Introduction to data mining and a data mining package
- Linear Models
- Classification and regression trees
- Random forests and boosting
- Neural networks for classification and prediction
- Self-organising maps
- Cluster analysis
- Memory Based Reasoning
- Support Vector Machines
- Text Mining
Please note: this subject was previously titled Statistical Marketing Tools
This subject introduces the principles and techniques used by data miners. Data mining is used to add value to large collections of data, delivering discoveries that continue to revolutionise lives in our data-rich but knowledge-hungry world.
Please note: assessment values are indicative only, details will be advised at the start of the subject.
- Invigilated Exam (50%)
- Quizzes — Online (10%)
- Assignments — 2 (40%)
For textbook details check your university's handbook, website or learning management system (LMS).
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- QS Ranking 2024:
- 19
- Times Higher Education Ranking 2024:
- 14
Entry requirements
To enrol in this subject, you must be admitted into a degree.
Prior study
You must have successfully completed the following subject(s) before starting this subject:
one of
- SWI-STA70002-Multivariate Statistics
SWI-HMS780 (Not currently available)
Equivalent subjects
You should not enrol in this subject if you have successfully completed any of the following subject(s) because they are considered academically equivalent:
SWI-HMS794 (Not currently available)
Additional requirements
- Other requirements -
You will require access to a recent version of SPSS and the latest version of SAS Enterprise Miner. Note: SAS will only run on a Windows platform.
Study load
- 0.125 EFTSL
- This is in the range of 10 to 12 hours of study each week.
Equivalent full time study load (EFTSL) is one way to calculate your study load. One (1.0) EFTSL is equivalent to a full-time study load for one year.
Find out more information on Commonwealth Loans to understand what this means to your eligibility for financial support.