This course provides an overview of recent statistical theory that addresses topics such as high-dimensionality, large sample sizes, sequential prediction, incremental and parallel statistical learning. The goal of this is course is build on the knowledge developed in Statistical Learning (STAT3040/STAT7040) in order to understand new and effective methods for analysing Big Data. Particular focus will be given to methods for accurately predicting future observations in order to make decisions and for gaining insight into the relationships that exist between features and responses. Further, this course will also develop an understanding of how large sample sizes affect heterogeneity and commonality across different subpopulations present in the data.
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Tuition fees are for the academic year indicated at the top of the page.
If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
- Student Contribution Band:
- Unit value:
- 6 units
If you are an undergraduate student and have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). You can find your student contribution amount for each course at Fees. Where there is a unit range displayed for this course, not all unit options below may be available.
Offerings and Dates
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|9358||18 Jul 2016||29 Jul 2016||31 Aug 2016||28 Oct 2016||In Person||N/A|