| Semester 1, 2020 Online | |
| Short Description: | Data Mining |
| Units : | 1 |
| Faculty or Section : | Faculty of Health, Engineering and Sciences |
| School or Department : | School of Sciences |
| Student contribution band : | Band 2 |
| ASCED code : | 020199 - Computer Science not elsewhere |
| Grading basis : | Graded |
Staffing
Examiner:
Requisites
Pre-requisite: (STA2300 or STA8170) and CSC1401
Other requisites
It's is highly recommended that the students who take this course have proficiency in at least one of the mainstream programming languages such as C/C++, Java, Python and so on in order to carry out the project for this course.
Rationale
Data mining is an interdisciplinary field which brings together techniques of machine learning, database, information retrieval, mathematics and statistics. These techniques are used to find useful patterns in large datasets. Methods for such knowledge discovery in data bases are required owing to the size and complexity of data collection in administration, business and science.
Synopsis
Data mining aims at finding useful regularities or patterns in large data sets generated in modern management and science. This course covers the main data mining methods, including clustering, classification, association rules mining, and recent techniques for data mining. The methods are developed and applied to various data sets.
Objectives
On successful completion of this course students should be able to:
- Demonstrate advanced and integrated understanding of the basic data mining tasks and concepts
- Analyse critically and evaluate data mining problems
- Apply knowledge and skills to key algorithms in data mining applications
- Apply knowledge, expert judgement and problem-solving skills to real world data mining problems
- Analyse critically and reflect on the effectiveness and estimate the performance of data mining algorithms.
Topics
| Description | Weighting(%) | |
|---|---|---|
| 1. | Data pre-processing and preparation | 10.00 |
| 2. | Associate rule mining | 20.00 |
| 3. | Descriptive data mining (Clustering) | 20.00 |
| 4. | Predictive data mining (Classification) | 30.00 |
| 5. | Outlier Detection | 20.00 |
Text and materials required to be purchased or accessed
ALL textbooks and materials available to be purchased can be sourced from (unless otherwise stated). (https://omnia.usq.edu.au/textbooks/?year=2020&sem=01&subject1=CSC8004)
Please for alternative purchase options from USQ Bookshop. (https://omnia.usq.edu.au/info/contact/)
Reference materials
Student workload expectations
| Activity | Hours |
|---|---|
| Assessments | 58.00 |
| Directed 精东传媒app | 39.00 |
| Private 精东传媒app | 73.00 |
Assessment details
| Description | Marks out of | Wtg (%) | Due Date | Notes |
|---|---|---|---|---|
| Assignment 1 | 15 | 10 | 22 Apr 2020 | |
| Assignment 2 | 15 | 20 | 21 May 2020 | |
| Course research project | 100 | 70 | 19 Jun 2020 |
Important assessment information
-
Attendance requirements:
There are no attendance requirements for this course. However, it is the students鈥� responsibility to study all material provided to them or required to be accessed by them to maximise their chance of meeting the objectives of the course and to be informed of course-related activities and administration. -
Requirements for students to complete each assessment item satisfactorily:
To satisfactorily complete an individual assessment item a student must achieve at least 50% of the marks for that item. -
Penalties for late submission of required work:
Students should refer to the Assessment Procedure (point 4.2.4) -
Requirements for student to be awarded a passing grade in the course:
To be assured of receiving a passing grade a student must achieve at least 50% of the total weighted marks available for the course. -
Method used to combine assessment results to attain final grade:
The final grades for students will be assigned on the basis of the aggregate of the weighted marks obtained for each of the summative items for the course. -
Examination information:
NO EXAM: There is no examination in this course. -
Examination period when Deferred/Supplementary examinations will be held:
NO EXAM: There is no examination in this course, there will be no deferred or supplementary examinations. -
精东传媒app Student Policies:
Students should read the USQ policies: Definitions, Assessment and Student Academic Misconduct to avoid actions which might contravene 精东传媒app policies and practices. These policies can be found at .