Computational Statistical Methods (STAT5003)
UNIT OF STUDY
The objectives of this unit of study are to develop an understanding of modern computationally intensive methods for statistical inference, exploratory data analysis and data mining. Advanced computational methods for statistics will be introduced, including univariate, multivariate and combinatorial optimisation methods and simulation methods, such as Gibbs sampling, the Bootstrap, Monte Carlo and the Jackknife approach. In addition, the unit will demonstrate how to apply the above techniques effectively for use on large data sets in practice. Finally, this unit will show how to make inferences about populations of interest in data mining problems.
Our courses that offer this unit of study
- Graduate Certificate in Information Technology
- Graduate Certificate in Information Technology Management
- Graduate Diploma in Health Technology Innovation
- Graduate Diploma in Information Technology
- Graduate Diploma in Information Technology Management
- Master of Data Science
- Master of Health Technology Innovation
- Master of Information Technology
- Master of Information Technology Management
- Master of Information Technology and Master of Information Technology Management
Further unit of study information
Two lectures and one tutorial per week.
2 hour examination (60%), assignments (20%), quizzes (20%)
Computational Statistics, Geof H. Givens, Jennifer A. Hoeting, Wiley (2005)
Faculty/department permission required?
Unit of study rules
Study this unit outside a degree
If you wish to undertake one or more units of study (subjects) for your own interest but not towards a degree, you may enrol in single units as a non-award student.
If you are from another Australian tertiary institution you may be permitted to underake cross-institutional study in one or more units of study at the University of Sydney.