MIT9307 Data Management, Modelling and Mining
Unit Code
MIT9307
Level
Postgraduate
Campus
Sydney
Prerequisites
MIT9305
Credit Points
10
Subject Description
MIT9307 Data Management, Modelling, and Mining focuses on the principles and practices of handling data throughout its lifecycle, the conceptual and physical structuring of data, and the extraction of valuable information from large datasets. This subject covers statistical description of data and visualisation, data processing, and modelling concepts to ensure robust data analysis design. The subject also covers data mining methodologies such as pattern mining, clustering, classification, and outlier detection, incorporating the use of industry standard frameworks. Through practical exercises with hands-on experience, students will develop skills to discover patterns and insights using data management and mining tools to solve real-world problems securely and ethically.
Course Level
Level 2
Subject Type
☒ Specialisation (MIT Data Science)
☒ Elective (MIT)
Study Mode: Face to face on site
Study Load: Full-time
Learning Topics
- Data management, privacy and ethics
- Data cleaning, integration and transformation
- Database and data-warehouse modelling
- Pattern mining, classification and clustering
- Outlier detection and data-mining applications
Subject Learning Outcomes
- Demonstrate advanced knowledge of data management concepts, including data storage, quality, data privacy, ethical considerations and lifecycle management.
- Critically compare and analyse the suitability of data mining techniques to solve practical problems in various domains leveraging secure data privacy practices.
- Compare and interpret the results of data mining techniques such as predictive models assessing their performance to support informed and deep decision making.
- Implement data-driven solutions to real-world challenges, ensuring privacy and security management while enhancing decision-making through ethical and responsible data science applications.
Assessment
AIIHE uses a variety of assessment tools to guide and assess each student’s achievement of the subject learning outcomes. In this subject, students undertake portfolio students will compile a collection of their work that showcases their critical proficiency in managing data, creating data models, and performing data mining, individual report students must submit an individual 1500-words report assessing their expertise in effectively mining raw data, applying methods, and critically comparing the results for the suitability of different models in different problem domains to provide recommendation to support effective decision making, and group report and presentation students will create groups to analyse real world data sets and demonstrate their deep understanding and comprehension of data management, data modelling, data mining, and their implications in complex organisational processes. These tasks assess the knowledge, applied skills, critical analysis, communication and collaborative capabilities relevant to the subject.
Summary of Assessment
- Students must attempt all assessment tasks, and achieve an overall mark of at least 50%, to pass this subject.
- Assessment in this subject is consistent with and informed by the AIIHE Assessment Policy and Procedure.
| Assessment Type | Due Date | Weighting |
|---|---|---|
| Assessment 1: Portfolio | Week 2, 3, 4, 5, 6, 9, 10, 11 | 40% |
| Assessment 2: Individual Report | Week 8 | 20% |
| Assessment 3: Group Report and Presentation | Week 12, 13 | 40% |
Learning Resources
AIIHE will update the topic coverage of the subject and the available relevant learning resources at the time of subject delivery. In the interim, AIIHE provides the following indicative list of relevant reference material. AIIHE also subscribes to eLearning Resources through the Canvas portal, and where relevant material is available, AIIHE will seek to source reference material from this system for staff and student access.
Prescribed Reading
- Han, J., Jian, P., Hanghang, T. (2022). Data mining: concepts and techniques. Morgan Kaufmann. Available on May 3, 2024 from https://archive.org/details/the-morgan-kaufmann-series-in-data-management-systems-jiawei-han-micheline-kambe/page/n5/mode/2up.
Texts and References
- Tan, P. N., Steinbach, M., Kumar, V. (2016). Introduction to data mining. Pearson Education.
- Li, X., Zaiane, O.R., Li, Z. (2022). Advanced data mining and applications. Springer Publishing.