Unit rationale, description and aim
Organisations today face the challenge and opportunity of leveraging vast amounts of data to derive actionable insights. Data mining is a critical process for discovering patterns, relationships, and trends within large and complex datasets to support strategic and operational decision-making.
This unit builds on foundational studies in data science and programming, and provides a comprehensive exploration of the knowledge discovery process, incorporating essential topics such as data pre-processing, classification, clustering, association rule mining, anomaly detection, sentiment analysis, and sequential pattern mining. Emphasis is placed on applying both descriptive and predictive data mining techniques to solve real-world problems, using visual data mining tools (e.g. RapidMiner and Orange) and Python programming.
Case studies and applied projects will help students design, implement, and evaluate end-to-end data mining workflows, applying ethical principles and critically reflecting on issues of data privacy, bias, and explainability.
The primary aim of this unit is to develop industry-ready professionals capable of applying data mining techniques and tools to support informed and ethical decision-making across diverse domains.
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.
Evaluate a range of data mining techniques and sel...
Learning Outcome 01
Design data mining workflows using industry-standa...
Learning Outcome 02
Collaboratively analyse complex real-world dataset...
Learning Outcome 03
Critically assess the ethical, legal, and societal...
Learning Outcome 04
Content
Topics will include:
1. Foundations of Data Mining
· Introduction to Data Mining Concepts and Applications
· The Knowledge Discovery Process
· Data Warehousing and Online Analytical Processing (OLAP)
· Data Pre-processing and Statistical Foundations
2. Data Mining Techniques and Algorithms
· Pattern Mining and Association Rule Mining
· Classification Methods
· Feature Selection and Engineering
· Cluster Analysis
· Anomaly Detection
· Dimensionality Reduction Techniques
3. Specialised Applications
· Text Mining and Sentiment Analysis
· Time-Series and Sequential Pattern Mining
4. Model Evaluation and Deployment
· Model Evaluation Metrics and Cross-Validation Techniques
· Deployment Considerations in Real-World Environments
5. Interpretability/Explainability and Responsible AI
· Interpretable Models and Explainable AI (XAI) in Data Mining
· Responsible Data Mining: Ethical, Legal, and Privacy Considerations
6. Tools and Practical Implementation
· Python (Pandas, Scikit-learn, SHAP,…)
· RapidMiner/Orange
7. Case Studies and Industry Applications
Assessment strategy and rationale
Assessment tasks are designed to progressively build students' practical data mining capabilities, reinforce ethical reasoning, and prepare them for workplace challenges. All assessment tasks are mapped to the unit’s learning outcomes, and students must achieve a minimum aggregate mark of 50% to pass the unit.
Assessment Task 1 involves a series of hands-on exercises in data cleaning, transformation, exploratory data analysis, and core data mining tasks, aligning with foundational analytical and technical skills.
Assessment Task 2 requires students to apply descriptive data mining techniques and critically reflect on ethical, professional, and societal implications, supporting the development of responsible practice.
Assessment Task 3 focuses on predictive modelling, where students will build and evaluate models and explore specialised applications such as text mining and sentiment analysis.
Use of generative AI tools is permitted only where explicitly authorised within individual assessment instructions. Misuse will be treated in line with the university’s academic integrity policy.
All tasks will be assessed using detailed rubrics to ensure consistency, transparency, and alignment with the AQF Level 9 expectations.
Overview of assessments
Assessment Task 1: Practical exercises This asse...
Assessment Task 1: Practical exercises
This assessment consists of hands-on data mining exercises, including data pre-processing, exploratory data analysis and visualisation, model building and model evaluation using Phyton and a data mining tool (e.g. RapidMiner/Orange).
Submission Type: Individual
Assessment Method: Practical task
Artefact: Lab portfolio (code, program files, documentation and result interpretation)
30%
Assessment Task 2: Descriptive Data Mining The ...
Assessment Task 2: Descriptive Data Mining
The primary purpose of this assessment is to provide students with an opportunity apply descriptive data mining techniques and critically reflect on advantages and disadvantages of particular data mining solutions to solve real life problems.
Submission Type: Individual
Assessment Method: Case studyPractical task
Artefact: Written report (500 Words + charts/tables) + Code/Program files + Online Viva
30%
Assessment Task 3: Capstone Data Mining Project ...
Assessment Task 3: Capstone Data Mining Project
The purpose of this assessment is to provide students with an opportunity to develop data mining skills by designing and implementing an end-to-end data mining process to analyse a real-world dataset. In this assignment, student will perform data cleaning/transformation, exploratory data analysis and cluster analysis on a dataset. They also required to build and evaluate predictive models, and perform several advanced data ming tasks (e.g. Association Rule Mining, Anomaly Detection, Sentiment Analysis) . In this task students are required to interpret the results using XAI methods and address ethical implications of data mining in the context of the case study.
50% Group (collective) mark – awarded for the overall quality of the group work.
50% Individual mark – awarded based on each student’s demonstrated contribution.
Submission Type: Group
Assessment Method: Practical task and Online Viva
Artefact: Code + notebook comments+ live/speaker view webcam overlay recorded presentation (10 minutes) + Online Viva
40%
40%
Learning and teaching strategy and rationale
This unit is delivered through Attendance and Online modes using a single, integrated learning and teaching strategy designed to ensure equivalent learning outcomes and a comparable learning experience for all students, while supporting diverse learning needs and maximising access.
Across both modes, learning activities are intentionally aligned to the unit learning outcomes and assessment tasks, and are underpinned by active learning, guided engagement with disciplinary knowledge, opportunities for peer interaction, and regular, timely feedback. While the mode of delivery shapes how students participate, the pedagogical intent, expectations and standards remain consistent.
In Attendance mode, students engage in weekly face-to-face classes at designated locations, supported by preparatory activities prior to workshops and opportunities for consolidation following classes. Online learning platforms are used to complement face-to-face teaching through additional resources and learning activities.
In Online mode, students engage with the same core content and learning outcomes through a combination of synchronous and asynchronous activities, including structured discussions and applied learning tasks that support learning in professional contexts