• Offered by Rsch Sch of Finance, Actuarial Studies & App Stats
  • ANU College ANU College of Business and Economics
  • Course subject Statistics
  • Areas of interest Statistics

This course offers an introduction to modern statistical approaches for complicated data structures, and is designed for students who need to do advanced statistical data analyses and statistical research. There has been a prevalence of “big data” in many different scientific fields. In order to tackle the analysis of data of such size and complexity, traditional statistical methods have been reconsidered and new methods have been developed for extracting information, or "learning", from such data. Due to the wide of range of topics which could be considered, this course, each offering, will cover only a few of the potential topics. Some of the topics that may be considered are: regularisation and dimension reduction, clustering and classification, non-independent data, and causality. Emphasis is placed on methodological understanding, empirical applications, as well as theoretical foundations to a certain degree. As the extensive use of statistical software is integral to modern data analysis, there will be a strong computing component in this course.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Describe the rationale behind the formulation and components of a statistical model.
  2. Compare and contrast statistical models in the context of a particular scientific question.
  3. Communicate statistical ideas to a diverse audience.
  4. Formulate a statistical solution to real-data research problems.
  5. Demonstrate an understanding of the theoretical and computational underpinnings of various statistical procedures, including common classes of statistical models.
  6. Utilise computational skills to implement various statistical procedures.

Indicative Assessment

  1. Assignments (50) [LO 1,2,3,4,5,6]
  2. Final Exam (50) [LO 1,2,3,4,5,6]

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.

Workload

Students are expected to commit at least 10 hours per week to completing the work in this course. This will include at least 3 hours lecture classes, 1 hour tutorial class and up to 6 hours of private study time.

Inherent Requirements

Not applicable

Requisite and Incompatibility

To enrol in this course you must have completed STAT3040.

Prescribed Texts

Trevor Hastie, Robert Tibshirani and Jerome Friedman (2008). The Elements of Statistical Learning (Data Mining, Inference, and Prediction). 2nd Edition. Springer.


Fees

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:
2
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.

Units EFTSL
6.00 0.12500
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

The list of offerings for future years is indicative only.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.

Second Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
9601 27 Jul 2020 03 Aug 2020 31 Aug 2020 30 Oct 2020 In Person N/A

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