Predictive Analysis
Credit points
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
VU Brisbane
VU Sydney
Prerequisites
NIT2251 - Machine Learning and Data Mining
(Or equivalent to be determined by unit coordinator)
Co-requisites
(Corequisite units must be studied concurrently with this unit)
Overview
In this unit, you will employ AI tools to tackle complex data challenges using methods such as regression, classification, clustering, and time series analysis, enabling data-driven decision-making. You will collaborate with group members to analyse diverse datasets. In addition, you will explore the deployment of predictive models to ensure their effectiveness and scalability. Ethical considerations and professional standards will also be integrated throughout the unit to prepare you for the responsible application of predictive analytics in various domains. By the end of the unit, you will be prepared for roles in data science, business analytics, or further specialised study in advanced analytics.
Learning Outcomes
On successful completion of this unit, students will be able to:
- Design predictive analytics techniques, including regression, classification, clustering, and time series analysis, to solve complex business problems;
- Evaluate diverse datasets, identifying key patterns and features to enhance predictive modelling;
- Judge predictive models using validation techniques and performance metrics to improve accuracy and reliability;
- Integrate ethical and professional standards in the development, deployment, and interpretation of predictive models across various domains;
- Synthesise predictive insights through data visualisations and reports to support decision-making and strategic planning individually and as part of a team.
Assessment
For Melbourne campuses
Assessment type: Laboratory Work | Grade: 20%
Practical lab report
Assessment type: Assignment | Grade: 45%
Data mining project with demonstration, presentation and oral Q&A
Assessment type: Case Study | Grade: 35%
Scenario-based problem solving with Q&A verification
Laboratory Work: Students complete practical predictive analytics tasks and submit a lab report documenting their findings and outcomes.
Assignment: Includes structured milestone reviews and in-class progress checks. Student's individual contributions are logged, validated, and justified through recorded peer review or demonstration to ensure authenticity.
Case Study: Students apply concepts to unpredictable data science scenarios. Responses are evaluated based on reasoning, method selection, and outcome justification, with an in-class component or oral defence verifying understanding.
Required reading
Required and recommended readings will be made available on VU Collaborate.
As part of a course
This unit is studied as part of the following course(s):