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
Analyse complex real-world datasets to address dat...
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 study and Practical task
Artefact: Written report (500 Words + charts/tables) + Code/Program files
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.
Submission Type: Individual
Assessment Method: Practical task
Artefact: Written report (1300 words) + Code notebook/ including Q&AProgram files + Presentation (8-10 minutes)
40%
Learning and teaching strategy and rationale
This unit is delivered in multiple modes. Students should expect to commit approximately 150 hours over a 12-week semester, including class activities, readings, discussions, and assessments.
Blended Mode includes two-hour weekly workshops and two-hour fortnightly labs. Workshops use active learning strategies to explore key concepts through discussion, collaborative exercises, and applied problem-solving. Labs adopt an experiential learning approach, providing hands-on opportunities to design, test, and troubleshoot data mining workflows using industry tools. Digital platforms support preparation, practice, and consolidation of learning between sessions.
Online and ACU Online Modes adopt an active, student-centred learning approach. Students engage with e-modules, pre-recorded lectures, guided readings, and asynchronous discussions. Weekly activities are designed to build progressively from foundational concepts to applied analysis, encouraging peer interaction and real-world application. Regular, timely feedback supports individual learning progression.
All delivery modes aim to scaffold knowledge development, enhance engagement, and foster reflective, ethical, and professional data practices through active and problem-based learning.