Unit rationale, description and aim
In our digital age, vast amount of data is collected and stored at an enormous speed and in a variety of formats. Organisations across various sectors increasingly realise the benefits of exploiting raw data to generate useful knowledge. Data mining is the process of discovering meaningful patterns in large data sets. Data mining utilises techniques from various fields including Statistics, Machine Learning, Artificial Intelligence, and Database Systems to transform data into a comprehensible structure.
In this unit you will learn the foundational data mining concepts and techniques for various data mining tasks such as predictive modelling, association analysis, cluster analysis and anomaly detection. Also, you will learn how to use data mining tools to perform data mining tasks on real-world datasets
The primary aim of this unit is to equip students with the knowledge and skills required to perform data mining using state-of-the art tools and techniques to solve the real-world problems and enable informed decision making considering ethical perspectives such as subsidiarity, stewardship of resources, and human dignity.
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.
Explain various computational and statistical tech...
Learning Outcome 01
Discuss ethical perspectives in data mining such a...
Learning Outcome 02
Apply data mining tools and techniques to generate...
Learning Outcome 03
Develop and evaluate predictive data mining models...
Learning Outcome 04
Content
Topics will include:
- Data Mining & Knowledge Discovery Process
- Data Pre-processing and Data quality
- Classification Analysis
- Association Analysis
- Cluster Analysis
- Anomaly Detection
- Avoiding False Discoveries
- Data mining tools (e.g. Rapid Miner)
- Data mining ethics
Assessment strategy and rationale
To pass this unit, students are required to achieve an aggregate mark of at least 50%. Marking will be in accordance with a rubric specifically developed to measure the level of achievement of the learning outcomes for each item of assessment.
The assessment strategy for this unit is based on the need to determine authentic student achievement of the learning outcomes. Assessment methods incorporate problem-based tasks, case studies and practical/hands-on tasks that are relevant to the real-world needs. The first assessment provides students with an opportunity to perform data cleaning/transformation, exploratory data analysis and cluster analysis on a dataset and produce descriptive models using a data mining tool (e.g. RapidMiner). In assessment task 2, students will apply predictive data mining techniques to build and evaluate predictive models. In assessment task 3, students will apply predictive data mining techniques to build and evaluate predictive models.
Overview of assessments
Assessment Task 1 : Developmental Exercises This...
Assessment Task 1: Developmental Exercises
This assessment consists of a series of weekly exercises, including data cleaning/transformation, exploratory data analysis, cluster analysis and predictive model building using a data mining tool (e.g. RapidMiner).
The feedback from this assessment will help to develop students’ skills in data mining and apply them in the next assessments.
Submission Type: Individual
Assessment Method: Practical task
Artefact: Program files
25%
Assessment Task 2: Exploratory Data Mining Proje...
Assessment Task 2: Exploratory Data Mining Project
The primary purpose of this assessment is to provide students with an opportunity to develop data mining skills for finding human-interpretable patterns that describe the data analysis skills. In this assignment, student will perform data cleaning/transformation, exploratory data analysis and cluster analysis on a dataset, using a data mining tool. In this task students will also apply the ethical principles of data mining in the context of the case study.
To ensure academic integrity student are required to present their work in class or record and submit a video presentation.
Submission Type: Individual
Assessment Method: Practical task
Artefact: Written report + Program file + presentation
30%
Assessment Task 3: Predictive Data Mining Projec...
Assessment Task 3: Predictive Data Mining Project
The primary purpose of this assessment is to provide students with an opportunity to develop data predictive data mining skills. In this assignment, student will build and evaluate predictive models, detect anomaly and find association between variables in the given datasets using a data mining tool (RapidMiner). In this task students will also apply the ethical principles of data mining in the context of the case study.
To ensure academic integrity student are required to present their work in class or record and submit a video presentation.
Submission Type: Individual
Assessment Method: Practical task
Artefact: Written report + Program file + presentation + Online Viva
45%
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.
Across both delivery modes, students should plan to commit approximately 150 hours to this unit over the semester, including participation in learning activities, independent study, readings and assessment preparation.