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.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Upon successful completion of the requirements for this course, students should have theknowledge and skills to:
• Discuss in detail the rationale behind the formulation and components of a statistical
model.
• Analytically describe and implement approaches to compare and contrast statistical
models in the context of a particular scientific question.
• Communicate complex statistical ideas and heuristics to a diverse audience.
• Develop, analytically describe, and implement a statistical solution to real-data
research problems.
• Demonstrate an in-depth level interpretation of modeling results.
• Discuss in detail 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
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 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2017 | $3660 |
- International fee paying students
| Year | Fee |
|---|---|
| 2017 | $4878 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
First Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 4812 | 20 Feb 2017 | 27 Feb 2017 | 31 Mar 2017 | 26 May 2017 | In Person | N/A |
