Overview
Many problems in business including sales and inventory forecasting, credit scoring, recommender systems in online commerce and fraud detection use advanced tools for data analytics. This unit covers some of the most popular tools that may include tree-based methods, boosting, bagging, support vector machines, neural networks and deep learning. The … For more content click the Read More button below.
Offerings
S1-01-CAULFIELD-BLENDED
Requisites
Prerequisite
Prohibition
Rules
Enrolment Rule
Contacts
Chief Examiner(s)
Dr Ruben Loaiza Maya
Learning outcomes
On successful completion of this unit, you should be able to:
1.
understand different techniques used in business analytics and to be able to compare these from a statistical and computational point of view
2.
frame problems in finance, marketing, economics and related areas so that they can be solved by modern tools in business analytics
3.
implement machine learning methods in a modern software environment (for example, R) with potentially large datasets
4.
explain and interpret the analyses undertaken in a clear and effective manner and be aware of the limitations of these analyses.
Teaching approach
Active learning
Assessment
1 - Within semester assessment
2 - Examination
Scheduled and non-scheduled teaching activities
Seminars
Tutorials
Workshops
Workload requirements
Workload
Learning resources
Technology resources
Other unit costs
Costs are indicative and subject to change.
Electronics, calculators and tools: $100