Learning Outcomes

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

1. Explain and use basic financial statistical techniques and concepts to analyse financial and investment data;
2. Solve problems using the principles of probability;
3. Recognise and use different statistical distributions;
4. Perform calculations and interpret results of a variety of estimation techniques;
5. Conduct and explain the results of a hypothesis test;
6. Carry out and interpret an analysis of variance test and compare the difference between two or more sets of data; and,
7. Apply and interpret regression models.

Other Information

Offerings of this course outside of Semester 1 and Semester 2 are available only to students enrolled in the Master of Applied Data Analytics.

Indicative Assessment

1. Typical assessment may include, but is not restricted to: exams, assignments, quizzes, presentations and other assessment as appropriate (100) [LO 1,2,3,4,5,6,7]

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. 

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 130 hours of work in completing this course. This includes time spent in scheduled classes and self-directed study time.

Inherent Requirements

Not applicable

Requisite and Incompatibility

Prescribed Texts

Information about the prescribed textbook will be available via the Class Summary.

Specialisations

Offerings, Dates and Class Summary Links