- Code STAT3040
- Unit Value 6 units
- 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
- Academic career UGRD
- Mode of delivery In Person
First Semester 2017
See Future Offerings
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 areas such as finance, marketing, social networks and the scientific fields. As traditional statistical methods have become inadequate for analysing data of such size and complexity, this has led to the development of new statistical methods 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 such as the bootstrap, regularisation methods, tree based methods and unsupervised learning techniques such as clustering. As much modern data analysis requires the use of statistical software, there will be a strong computing component in this course.
Upon successful completion, students will have the knowledge and skills to: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
• Compare and contrast statistical models in the context of a particular scientific
• 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 AssessmentTypical 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.
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
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, Dates and Class Summary Links
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|4810||20 Feb 2017||27 Feb 2017||31 Mar 2017||26 May 2017||In Person||N/A|