MA3405 - Statistical Data Mining for Big Data
| Credit points: | 03 |
| Year: | 2017 |
| Student Contribution Band: | Band 2 |
| Administered by: | College of Science and Engineering |
Provide students with practical skills in statistical data mining methods, essential for extracting information from multivariate, highly dimensional, common to the "Big Data" world we live in today. The following topics will be covered: Introduction to multivariate analysis; Cluster analysis; The multivariate normal distribution; Maximum likelihood estimation; Comparison of multivariate means; Principal components; Factor analysis; Discriminant analysis.
Learning Outcomes
- to give the student a firm understanding of statistical methods, essential for extracting information from multivariate and "real world" data.
| Prerequisites: | MA2405 OR MA2000 |
Availabilities | |
| Townsville, Internal, Study Period 2 | |
| Census Date 24-Aug-2017 | |
| Coord/Lect: | Assoc. Professor Yvette Everingham. |
| Contact hours: |
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| Assessment: | end of semester exam (70%); other exams (10%); assignments (20%). |
Note: Minor variations might occur due to the continuous Subject quality improvement process, and in case of minor variation(s) in assessment details, the Subject Outline represents the latest official information.