- Code STAT4050
- 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 Statistics
- Academic career UGRD
- Dr Yanrong Yang
- Mode of delivery In Person
- Co-taught Course
Second Semester 2020
See Future Offerings
All activities that form part of this course will be delivered remotely
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.
Upon successful completion, students will have the knowledge and skills to:
- Describe the rationale behind the formulation and components of complex statistical models.
- Compare and contrast statistical models in the context of a variety of scientific questions.
- Communicate complex statistical ideas to a diverse audience.
- Formulate a statistical solution to real-data research problems.
- Demonstrate an understanding of the theoretical and computational underpinnings of various statistical procedures, including common classes of statistical models.
- Utilise computational skills to implement various statistical procedures.
- Assignments (50) [LO 1,2,3,4,5,6]
- Final Exam (50) [LO 1,2,3,4,5,6]
In response to COVID-19: Please note that Semester 2 Class Summary information (available under the classes tab) is as up to date as possible. Changes to Class Summaries not captured by this publication will be available to enrolled students via Wattle.
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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.
Requisite and Incompatibility
You will need to contact the Rsch Sch of Finance, Actuarial Studies & App Stats to request a permission code to enrol in this course.
Trevor Hastie, Robert Tibshirani and Jerome Friedman (2008). The Elements of Statistical Learning (Data Mining, Inference, and Prediction). 2nd Edition. Springer.
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.
- Domestic fee paying students
- International fee paying students
Offerings, Dates and Class Summary Links
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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|
|9702||27 Jul 2020||03 Aug 2020||31 Aug 2020||30 Oct 2020||In Person||View|