6 points, SCA Band 3, 0.125 EFTSL
Undergraduate - 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
- First semester 2019 (On-campus)
Business analytics involves uncovering the hidden information in masses of business data using statistical models and algorithms. In this unit, some of the most widely used prediction and classification models will be covered. A suitable software environment for business analytics will be used, and tools for handling large data sets will be introduced.
We will explore the trade-off and distinction between prediction, explanation and interpretation using statistical models. Topics to be covered include numerical optimisation; Monte Carlo simulation; resampling methods such as the bootstrap, cross-validation, and bagging; nonlinear and nonparametric methods such as regression splines, trees and support vector machines; principal components analysis and clustering.
The learning goals associated with this unit are to:
- select and develop appropriate models for clustering, prediction or classification
- estimate and simulate from a variety of statistical models
- measure the uncertainty of a prediction or classification using resampling methods
- apply business analytic tools to produce innovative solutions in finance, marketing, economics and related areas
- manage very large data sets in a modern software environment
- explain and interpret the analyses undertaken clearly and effectively.
Within semester assessment: 40% + Examination: 60%
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