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
Dr Jill Wright
(Caulfield, Trimester A)
Associate Professor Colin Jevons (Trimester B)
Dr Jill Wright
Dr Lito Cruz (City)
- Summer semester B 2018 (On-campus block of classes)
- Trimester A 2018 (On-campus)
- Trimester B 2018 (On-campus)
Business analytics can unlock the hidden insights in data to give businesses a competitive advantage. Many businesses have masses of data about customers and operations, and need skilled analysts to uncover insights and make informed predictions.
This unit uses data visualisation to explore and analyse data sets of all sizes, and it introduces some business analytic models for interpretation and prediction.
It will introduce an appropriate software environment for data visualisation, and analytics, and cover visualisation and analysis techniques for categorical and numerical variables. Visualisation methods to be covered include some of Box-and-whisker plots, Mosaics, Rotatable 3D scatter plots, Heat maps, Motion charts, cluster and association charts. Models to be covered may include linear regression models, classification and regression trees, and random forests. Methods for evaluating model performance will also be discussed. Examples from marketing, finance, economics and related disciplines will be included.
The learning goals associated with this unit are to:
- select, create and interpret appropriate types of visual representation for a given set of data
- select and develop model types with explanatory and/or predictive ability
- make appropriate use of in-sample and out-of-sample evaluation of models
- apply the above research skills to produce innovative solutions in finance, marketing, economics and related areas
- use visualisation and modelling to effectively communicate the results of their investigations
- explain the sequence of procedures that should be applied to analyse a given dataset.
Within semester assessment: 50% + Examination: 50%
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 four hours of scheduled activities per week when taught in trimester mode, and three to four hours three times per week when taken as a summer unit. Scheduled activities may include a combination of teacher directed learning, peer directed learning and online engagement.
See also Unit timetable information