6 points, SCA Band 3, 0.125 EFTSL
Postgraduate - Unit
Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.
Department of Econometrics and Business Statistics
- Second semester 2019 (On-campus)
This course provides a strong foundation in data analytics and statistics for a business management career. Most management decisions are made under uncertain conditions and therefore need a framework to know why and how data analytics efforts should be implemented to optimise business resources. While technical analysis is essential, the content of this course is presented from the perspective of a future manager, rather than from the viewpoint of a statistician. Students will be exposed to real-world examples from the industry and use computer software to analyse data. Topics covered include descriptive, predictive and prescriptive analytics.
The learning objectives associated with this unit are:
- examine the different types of business data and how they can be visualised and related (e.g. charts, association and clustering analysis) to leverage business decision making.
- explore the potential uses of business data using different predictive modelling tools (e.g. regression, decision tress, classification, and forecasting) to estimate relationships among variables that can predict future outcomes and quantify economic trade-offs to qualify business decision making
- formulate recommendations and insights based on business data toward addressing various business problems and optimising business resources.
Within semester assessment: 60% + Examination: 40%
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning, peer directed learning and online engagement.
See also Unit timetable information