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Unit of study_
DATA5710: Applied Statistics for Complex Data
2021 unit information
With explosions in availability of computing power and facilities for gathering data in recent times, a key skill of any graduate is the ability to work with increasingly complex datasets. There may include, for example, data sets with multiple levels of observations gathered from diverse sources using a variety of methods. Being able to apply computational skills to implement appropriate software, as well as bringing to bear statistical expertise in the design of the accompanying algorithms are both vital when facing the challenge of analysing complicated data. This unit is made up of three distinct modules, each focusing on a different aspect of applications of statistical methods to complex data. These include (but are not restricted to) the development of a data product that interrogate large and complicated data structures; using sophisticated statistical methods to improve computational efficiency for large data sets or computationally intensive statistical methods; and the analysis of categorical ordinal data. Across all modules you will develop expertise in areas of statistical methodology, statistical analysis as well as computational statistics. Additional modules may be delivered, depending on the areas of expertise of available staff and distinguished visitors.
Unit details and rules
Managing faculty or University school:
Science
| Study level | Postgraduate |
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| Academic unit | Mathematics and Statistics Academic Operations |
| Credit points | 6 |
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Prerequisites:
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None |
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Corequisites:
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None |
| Prohibitions:
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None |
| Assumed knowledge:
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Familiarity with probability theory at 4000 level (e.g., STAT4211 or STAT4214 or equivalent) and with statistical modelling (e.g., STAT4027 or equivalent). Please consult with the coordinator for further information. |
At the completion of this unit, you should be able to:
- LO1. Demonstrate a coherent and advanced understanding of key concepts in computational statistics.
- LO2. Apply fundamental principles and results in statistics to solve given problems.
- LO3. Distinguish and compare the properties of different types of statistical models and statistical methods applicable to them.
- LO4. Identify assumptions required for various statistical methods to be valid and devise methods for testing these assumptions.
- LO5. Devise statistical solutions to complex problems.
- LO6. Adapt various computational techniques to build software for solving particular statistical problems.
- LO7. Communicate coherent statistical arguments appropriately to student and expert audiences, both orally and through written work.
Unit availability
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Important enrolment information
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