ITEC632 - Data Mining Techniques and Applications

Year

Credit points

Campus offering

Prerequisites

ITEC622 Data Analytics and Visualisation

Teaching organisation

150 hours over a twelve-week semester or equivalent study period

Unit rationale, description and aim

Organisations across various sectors increasingly realise the benefits of exploiting the enormous data they collect and store to generate useful knowledge. Data mining is an important analytical tool as organisations deal with increasingly large data sets. Data mining is the process of discovering meaningful patterns (i.e., knowledge) in large data sets and learning from data. The knowledge discovery process includes data exploration, data pre-processing, data analysis using statistical and machine learning techniques, and result visualisations.

 This unit will cover the data mining concepts and techniques for various data mining tasks. Also, this unit will illustrate the technologies applied in complex data mining by examples, including time-series data, sequential data and text data. Also, in this unit students gain practical data mining skills by applying a data mining tool (RapidMiner) to perform data mining tasks on real-world datasets.

 The primary aim of this unit is provide students with the knowledge of data mining concepts ad techniques and the skills required to perform data mining using no-code tools, to enable informed decision making considering ethical perspectives.

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 the benefit and applications of data mining (GA5, GA8)

LO2 - Critically reflect on advantages and disadvantages of particular data mining solutions to solve real life problems with considerations of data privacy and professional ethics (GA3, GA5)

LO3 - Apply data mining tools and exploratory, predictive, classification and segmentation data mining procedures in a variety of areas (GA5, GA10)

LO4 - Critically evaluate the output of 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 Concepts 
• Data Mining Applications
• Data pre-processing in preparation for building data mining models 
• Work with appropriate data mining tools efficiently 
• Model Building
• Supervised Learning Data Mining techniques
• Unsupervised Learning Data Mining techniques 
• Text Mining 
• Model evaluation and deployment 
• Data mining ethics and data privacy
• Data mining case studies

Learning and teaching strategy and rationale

This unit is offered in different modes to cater for the learning needs and preferences of a range of participants and maximise effective participation for isolated and/or marginalised groups.

Students should anticipate undertaking 150 hours of study for this unit over a twelve-week semester or equivalent study period, including class attendance, readings, online forum participation and assessment preparation.

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 you to prepare and revise.

Online Mode

This unit utilises an active learning approach whereby students will engage in e-module activities, readings and reflections, and opportunities to collaborate with peers in an online environment. This can involve, but is not limited to, online workshops, online discussion forums, chat rooms, guided reading, and webinars. To deliver core content, pre-recorded lectures will be incorporated within the online learning environment and e-modules. In addition, electronic readings will be provided to guide students’ reading and extend other aspects of online learning. 

ACU Online

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.

Assessment strategy and rationale

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

North, M. (2020) Data Mining for the Masses: With Implementations in RapidMiner and R, fourth Edition, MyEducator.

Olson, D. L. & Wu, D. (2020) Predictive Data Mining Models. 2nd ed. 2020. [Online]. Singapore: Springer Singapore.

Olson, D. L. & Lauhoff, G. (2019) Descriptive Data Mining. 2nd ed. 2019. [Online]. Singapore: Springer Singapore.

Kotu,V., Deshpande, B. (2019) Data Science: Concepts and Practice with RapidMiner, Morgan Kaufmann Publishers.

Jamsa, K. (2021) Introduction to Data Mining and Analytics, Jones & Bartlett Learning LCC.