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

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