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

This course provides an introduction to statistical learning and aims to develop skills in modern statistical data analysis. 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.  This course will cover a range of topics in statistical learning including linear regression, classification techniques, resampling methods (e.g., the bootstrap), regularisation methods, tree based methods, and unsupervised learning techniques (e.g., clustering). 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 of the requirements for this course, students should have the
knowledge and skills to:

• Understand the rationale behind the formulation and components of a statistical
model.
• Compare and contrast statistical models in the context of a particular scientific
question.
• Communicate complex statistical ideas to a diverse audience.
• Formulate a statistical solution to real-data research problems.
• Understand the theoretical and computational underpinnings of various statistical
procedures, including common classes of statistical models.
• Demonstrate computational skills to implement various statistical procedures.

Indicative Assessment

Typical assessment may include, but is not restricted to: assignments, project and a final exam.

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 contact hours per week and up to 7 hours of private study time.

Requisite and Incompatibility

To enrol in this course you must have completed STAT2001 and STAT2008

Majors

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:
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 and Dates

The list of offerings for future years is indicative only

First Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery
3991 25 Feb 2019 04 Mar 2019 31 Mar 2019 31 May 2019 In Person

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