Predictive analytics are a set of tools to enable managers to exploit the patterns found in transactional and historical data. For example major retailers invest in predictive analytics to understand, not just consumers' decisions and preferences, but also their personal habits, so as to more efficiently market to them. This unit introduces different techniques of data analysis and modelling that can be applied to traditional and non-traditional problems in a wide range of areas including stock forecasting, fund analysis, asset allocation, equity and fixed income option pricing, consumer products, as well as consumer behaviour modelling (credit, fraud, marketing). The forecasting techniques covered in this unit are useful for preparing individual business forecasts and long-range plans. The unit takes a practical approach with many up-to-date datasets used for demonstration in class and in the assignments.
1x 2hr lecture and 1x 1hr tutorial per week
assignment 1 (20%), assignment 2 (20%), mid-term exam (20%), final exam (40%)
This unit assumes mathematical knowledge at the level of the Maths in Business program (including calculus and matrix algebra) and basic computer programming skills at the level of QBUS2810.
QBUS2810 or ECMT2110 or DATA2002