Useful links
Unit of study_
QBUS3830: Advanced Analytics
This unit is designed to equip students with advanced tools for estimation and testing in relevant business statistical models. In particular, the unit covers maximum likelihood, Bayesian estimation and inference, and hypothesis testing. The unit acknowledges the importance of learning computing skills as helpful for job applications and special emphasis is made throughout the unit to learn numerical methods such as Monte Carlo simulations and Bootstrapping. Special topics in advanced statistical modelling, such as nonlinear estimators and time series regression, are also covered. The materials taught are essential as preparation for honours in Quantitative Business Analysis.
| Code | QBUS3830 |
|---|---|
| Academic unit | Business Analytics |
| Credit points | 6 |
|
Prerequisites:
?
|
QBUS2810 or DATA2002 or ECMT2110 |
|---|---|
|
Corequisites:
?
|
None |
| Prohibitions:
?
|
None |
At the completion of this unit, you should be able to:
- LO1. demonstrate the understanding of the underlying theory for advanced analysis of data arising in business contexts
- LO2. choose with success the most appropriate and relevant statistical tools for solving the business analytic problem of interest
- LO3. identify with accuracy and communicate the positives as well as the limitations of a range of analytical methods
- LO4. demonstrate an ability to extract relevant information from large volumes of business-related data available online
- LO5. demonstrate a high level of competence in statistical literacy and communicating the results of your analyses
- LO6. demonstrate proficiency in the use of at least one statistical software package: Matlab, R or Python.
Unit outlines
Unit outlines will be available 2 weeks before the first day of teaching for the relevant session.
- Semester 2, 2024 [Normal day] - Camperdown/Darlington, Sydney
- Key dates
Key dates through the academic year, including teaching periods, census, payment deadlines and exams.
- Student administration
Enrolment, course planning, fees, graduation, support services, student IT
- Expectations of student conduct
Code of Conduct for Students, Conditions of Enrollment, University Privacy Statement, Academic Integrity
- Academic appeals
Academic appeals process, special consideration, rules and guidelines, advice and support
- Learning and teaching policy
Policy register, policy search
- Financial support
Scholarships, interest free loans, bursaries, money management
- Study resources
Learning Centre, faculty and school programs, Library, online resources
- Health and support
Student Centre, counselling & psychological services, University Health Service, general health and wellbeing
