MIT632 Data Mining

Unit outline

Important Update: Our aim is to provide you with an optimal learning experience, regardless of how this unit is delivered. Teaching will be delivered in line with the most current COVID Safe health guidelines.  This may include a mix of online and face-to-face.  Please check the learning management system for announcements and updates. Thank you for your flexibility and commitment to studying with Sydney Institute of Higher Education. 
Enrolment modes: Year 2, Semester 1
Credit point(s): 12.5
EFTSL value: 0.125
Prerequisite: MIT502 Database Systems
Typical study commitment: Students will on average spend 10 hours per week over the teaching period undertaking the teaching, learning and assessment activities for this unit.
Scheduled learning activities: 4 timetabled hours per week, 6 personal study hours per week
Other resource requirements: Students will need access to lab computers or will need their own laptops in order to carry out lab exercises and assignments. Students will need to use R.

Unit description 

This unit covers the key concepts of data mining. Massive amounts of data are being generated by individuals, public and private organisations. The Internet provides a very large source of information about almost every aspect of human life and society. This unit focuses on methods, techniques and tools to collect, integrate, pre-process and process large amounts of data in order to find anomalies, trends, correlations and patterns in data sets which can in turn provide significant benefits to organisations.


Unit learning outcomes (ULO)   

 
On the successful completion of this units student will be able to:
ULO1 Evaluate the value and application of data mining for businesses.
ULO2 Identify appropriate data mining techniques and models for given business problems.
ULO3 Apply data pre-processing in preparation for building data mining models.
ULO4 Differentiate a supervised learning method from an unsupervised learning method.
ULO5 Apply appropriate data mining techniques and tools to datasets to find anomalies, trends, correlations, and patterns in datasets.

Topics to be included in the unit

1. Introduction to Data Mining
2. Features of Data
3. Data Pre-processing
4. Data Integration
5. Classification Methods
6. Frequent Patterns
7. Cluster Analysis
8. Association Analysis and Correlations
9. Anomaly Detection
10. Avoiding False Discoveries
11. Data Warehousing and Online Analytical Processing
12. Data Mining Applications & Revision

Assessment

Assessment Description Grading and weighting
(% total mark for unit)
Indicative due week 
Assessment 1: Class Participation 10% 12
Assessment 2: Online Quiz 10% 5
Assessment 3: Group Report 20% 11
Assessment 4: Individual Project 30% 12
Assessment 5: Final Exam 30% Final exam week

The assessment due weeks provided may change. Your lecturer will clarify the exact assessment requirements, including the due date, at the start of the teaching period.