Year
2024Credit points
10Campus offering
Prerequisites
ITEC202 Data Management and Visualisation
Teaching organisation
150 hours over a twelve-week semester or equivalent study period
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.
| Learning Outcome Number | Learning Outcome Description | Relevant Graduate Capabilities |
|---|---|---|
| LO1 | Explain various computational and statistical techniques for data mining and their applications | GC1, GC9 |
| LO2 | Discuss ethical perspectives in data mining such as subsidiarity, stewardship of resources, and human dignity | GC1, GC6 |
| LO3 | Apply data mining tools and techniques to generate human-interpretable patterns that describe the data | GC2, GC10 |
| LO4 | Develop and evaluate predictive data mining models | GC1, GC10 |
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
Learning and teaching strategy and rationale
This unit is offered in different modes (“Multi” mode and “Online” mode) to cater to the learning needs and preferences of a range of participants and maximise effective participation for isolated and/or marginalised groups.
Multi-Mode
In a multi-mode, students will require face-to-face attendance in blocks of time determined by the School. Students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. This unit is structured with required upfront preparation before workshops. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for students to prepare and revise.
Online Mode
This unit uses an active learning approach to support students in the exploration of knowledge essential to the discipline. Students are provided with choice and variety in how they learn. Students are encouraged to contribute to asynchronous weekly discussions. Active learning opportunities provide students with opportunities to practice and apply their learning in situations similar to their future professions. Activities encourage students to bring their own examples to demonstrate understanding, application and engage constructively with their peers. Students receive regular and timely feedback on their learning, which includes information on their progress.
Students should anticipate undertaking 150 hours of study for this unit, including class attendance, readings, online forum participation and assessment preparation.
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
| Brief Description of Kind and Purpose of Assessment Tasks | Weighting | Learning Outcomes |
|---|---|---|
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% | LO1, LO3, LO4 |
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 (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 | 30% | LO1, LO2, LO3 |
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 | 45% | LO1, LO2, LO3, LO4 |
Representative texts and references
Shmueli, G., Bruce, P.C., Yahav, I., Patel, N.R. and Lichtendahl Jr, K.C., 2017. Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons.
Jamsa, K. 2021 Introduction to Data Mining and Analytics, Jones & Bartlett Learning LCC.
North, M. 2018, Data Mining for the Masses, Third Edition: With Implementations in RapidMiner and R, CreateSpace Independent Publishing Platform.
Tan, P.N., Steinbach, M., Karpatne, A. and Kumar, V., 2019. Introduction to data mining, 2nd Edition, Pearson Education.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. 2017. Data mining: practical machine learning tools and techniques, Fourth edition, Elsevier.