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COIS20078 - Data Mining

General Information

Unit Synopsis

Data mining is the process of finding useful patterns in data, and this course examines the basics of data mining, model building and testing, and interpreting and validating results. Appropriate software is used by students to implement these ideas in practice. Students experience the theoretical and practical aspects of data mining.

Details

Level Postgraduate
Unit Level Not Applicable
Credit Points 8
Student Contribution Band SCA Band 2
Fraction of Full-Time Student Load 0.16666666666667
Pre-requisites or Co-requisites There are no pre-requisites for the unit.

Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit, should drop the subsequent unit before the census date or within 10 working days of Fail grade notification. Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability. See details in the Assessment Policy and Procedure (Higher Education Coursework).

Class Timetable View Unit Timetable
Residential School No Residential School

Unit Availabilities from Term 3 - 2025

There are no availabilities for this unit on or after Term 3 - 2025

Attendance Requirements

All on-campus students are expected to attend scheduled classes - in some units, these classes are identified as a mandatory (pass/fail) component and attendance is compulsory. International students, on a student visa, must maintain a full time study load and meet both attendance and academic progress requirements in each study period (satisfactory attendance for International students is defined as maintaining at least an 80% attendance record).

Assessment Overview

Recommended Student Time Commitment

Each 8-credit Postgraduate unit at CQUniversity requires an overall time commitment of an average of 16.666666666667 hours of study per week, making a total of 200 hours for the unit.

Assessment Tasks

Assessment Task Weighting

This is a graded unit: your overall grade will be calculated from the marks or grades for each assessment task, based on the relative weightings shown in the table above. You must obtain an overall mark for the unit of at least 50%, or an overall grade of ‘pass’ in order to pass the unit. If any ‘pass/fail’ tasks are shown in the table above they must also be completed successfully (‘pass’ grade). You must also meet any minimum mark requirements specified for a particular assessment task, as detailed in the ‘assessment task’ section (note that in some instances, the minimum mark for a task may be greater than 50%).

Consult the University's Grades and Results Policy for more details of interim results and final grades

Past Exams

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Previous Feedback

Term 1 - 2011 : The overall satisfaction for students in the last offering of this course was 83.33% (`Agree` and `Strongly Agree` responses), based on a 2.05% response rate.

Feedback, Recommendations and Responses

Every unit is reviewed for enhancement each year. At the most recent review, the following staff and student feedback items were identified and recommendations were made.

Source: Moodle
Feedback
Course Evaluation
Recommendation
There were 6 responses. As to overall satisfaction of the quality of the course 1 strongly agreed, 4 agreed and 1 strongly disagreed. The cause of strong disagreement was a misunderstanding of the exam advice received from the lecturer. Processes have been put in place such that such misunderstanding does not occur in future.
Action Taken
In Progress
Source: Individual responses sought from the students and lecturers by the Course Coordinator.
Feedback
Exam
Recommendation
There have been some confusion with the exam advice. It was discovered that happened because of old exam advice residing in the hard drives of different campuses, and some students failed to identify that those were old exam advices. To prevent any recurrence of the problem the hard drives would be cleared of old course contents.
Action Taken
In Progress
Unit learning Outcomes

On successful completion of this unit, you will be able to:

  1. understand the usage and limitations of data mining
  2. understand the statistical concepts for data mining
  3. apply data mining techniques appropriately and use data mining software
  4. discuss ethical and professional issues in data mining
  5. develop a general awareness of data warehouses and executive information systems
  6. understand what online analytical processing (OLAP) is and how it can be used to analyse data.

Alignment of Assessment Tasks to Learning Outcomes
Assessment Tasks Learning Outcomes
1 2 3 4 5 6
Alignment of Graduate Attributes to Learning Outcomes
• Professional Level
• Advanced Level
Graduate Attributes Learning Outcomes
1 2 3 4 5 6
Alignment of Assessment Tasks to Graduate Attributes
• Professional Level
• Advanced Level
Assessment Tasks Graduate Attributes
1 2 3 4 5 6 7 8