Unit rationale, description and aim
The ability to anticipate outcomes and make data-driven decisions is fundamental to success across industries. Predictive modelling and analytics leverage historical and current data through statistical, machine learning, and deep learning techniques to estimate future behaviour, identify patterns, and inform strategic decisions.
In this unit, students will explore predictive approaches across diverse data types and apply appropriate techniques to solve real-world problems. The unit emphasises reproducible modelling practices, feature engineering, model evaluation, and the communication of analytical results to stakeholders. Students will develop both technical proficiency and critical insight into the opportunities, limitations, and ethical considerations of predictive analytics in practice.
The aim of this unit is to equip students with the knowledge and practical skills to design, implement, and communicate predictive models that support informed decision-making and innovation across a range of domains.
Campus offering
No unit offerings are currently available for this unit.Learning outcomes
To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.
Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.
Explore the graduate capabilities.
Critically evaluate the use of predictive analytic...
Learning Outcome 01
Design reproducible predictive analytic solutions ...
Learning Outcome 02
Evaluate the selection, implementation and use of ...
Learning Outcome 03
Communicate predictive analytics solutions to stak...
Learning Outcome 04
Content
Topics will include:
- Introduction to Predictive Analytics and Data Types
- Data Fundamentals and Exploratory Analysis
- Data Preparation and Pre-processing for Predictive Modelling
- Feature Engineering and Predictive Models
- Machine Learning and Forecasting Techniques
- Deep Learning for Predictive Analytics
- Model Evaluation and Selection
- Deployment and Communication of Predictive Models
- Future Trends and Industry Applications
Assessment strategy and rationale
Assessments in this unit are designed to develop both conceptual understanding and practical skills in predictive analytics. The sequence scaffolds learning by moving from individual critical analysis to collaborative application using real-world data.
Assessment 1 focuses on the critical evaluation of predictive analytic methods and the design of a small-scale predictive model. Students demonstrate their understanding of the role, benefits and limitations of various approaches in supporting data-driven decision making.
Assessment 2 builds on this foundation through a group project involving the design, implementation, evaluation and communication of a complete predictive analytics solution to a business or industry problem. The group presentation enables students to demonstrate professional communication and reflect on ethical, technical and practical considerations.
Together, these assessments promote the integration of theoretical knowledge with applied skills and professional competencies.
To pass the unit, students must achieve all learning outcomes and obtain a minimum mark of 50%.
Overview of assessments
Assessment Task 1: Written Report and Computer P...
Assessment Task 1: Written Report and Computer Program
Students will critically evaluate and compare predictive-analytics techniques, discussing their benefits and limitations in supporting data-driven decision making.
40%
Assessment Task 2: Written Report, Presentation a...
Assessment Task 2: Written Report, Presentation and Q&A
Students will work collaboratively on a predictive-analytics project to design, implement and evaluate a Python-based model addressing a business or industry problem.
60%
Learning and teaching strategy and rationale
To develop the technical expertise and professional competencies required for vocational outcomes, students will engage in hands-on problem-solving activities that promote active learning, critical thinking, and collaborative engagement. This student-centred approach reflects the types of challenges graduates are likely to encounter in industry.
The unit combines asynchronous online materials with interactive and collaborative activities to support flexible and applied learning. Foundational knowledge is delivered through structured online content such as videos, guided exercises and self-paced quizzes, allowing students to engage with key concepts at their own pace. These materials are complemented by practical exercises and collaborative projects that enable students to apply theoretical knowledge to real-world problems.
Overall, this approach encourages students to think critically, work effectively in teams, and develop the practical skills and professional judgement required to design and implement predictive analytics solutions in diverse industry contexts.