ITEC325 - Applied Data Mining

Year

Credit points

Campus 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.

On successful completion of this unit, students should be able to:

LO1 - Explain various computational and statistical techniques for data mining and their applications (GA5, GA8)

LO2 - Discuss ethical perspectives in data mining such as subsidiarity, stewardship of resources, and human dignity (GA3, GA5)

LO3 - Apply data mining tools and techniques to generate human-interpretable patterns that describe the data (GA5, GA10)

LO4 - Develop and evaluate predictive data mining models (GA5, GA10)

Graduate attributes

GA3 - apply ethical perspectives in informed decision making

GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession 

GA8 - locate, organise, analyse, synthesise and evaluate information 

GA10 - utilise information and communication and other relevant technologies effectively.

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 (“Blended” 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.

Blended Mode

In a blended 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

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.