Multivariate Statistics for Data Science (MAST90138)
Graduate courseworkPoints: 12.5Dual-Delivery (Parkville)
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Overview
| Availability | Semester 2 - Dual-Delivery |
|---|---|
| Fees | Look up fees |
Modern statistics and data science deals with data having multiple dimensions. Multivariate methods are used to handle these types of data. Approaches to supervised and unsupervised learning with multivariate data are discussed. In particular, methods for classification, clustering, and dimension reduction are introduced, which are particularly suited to high-dimensional data. Both parametric and nonparametric approaches are discussed.
Intended learning outcomes
On completion of this subject, students will be able to:
- Understand the structure of multivariate data, particularly high-dimensional data.
- Select, implement, and justify appropriate methods to perform classification, clustering, and dimension reduction.
- Understand the statistical underpinnings of the methods used in multivariate data and be able to check when assumptions may or may not hold.
- Pursue further studies in this and related areas, or to be work ready as an applied statistician or a data scientist.
Generic skills
Students will be provided with the opportunity to practise and reinforce:
- Problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
- Analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
- Collaborative skills: the ability to work in a team;
- Time-management skills: the ability to meet regular deadlines while balancing competing commitments.
Last updated: 31 January 2024
Eligibility and requirements
Prerequisites
One of
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST30025 | Linear Statistical Models | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
OR
Note: the following subject/s can also be taken concurrently (at the same time)
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST90104 | A First Course In Statistical Learning | Semester 2 (Dual-Delivery - Parkville) |
25 |
OR
Admission into one of:
• Master of Data Science (MC-DATASC) - Statistics Background Stream
• Master of Data Science (MC-DATASC) - Data Science Background Stream
Corequisites
None
Non-allowed subjects
MAST90085 Multivariate Statistical Techniques
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 31 January 2024
Assessment
| Description | Timing | Percentage |
|---|---|---|
Up to 20 pages of written assignments (equivalent to approx. 20 hours)
| Early in the teaching period | 15% |
Up to 20 pages of written assignments (equivalent to approx. 20 hours)
| Mid semester | 15% |
Up to 20 pages of written assignments (equivalent to approx. 20 hours)
| Late in the teaching period | 15% |
Written examination
| During the examination period | 55% |
Last updated: 31 January 2024
Dates & times
- Semester 2
Principal coordinator Dennis Leung Mode of delivery Dual-Delivery (Parkville) Contact hours 36 hours comprising 2 one-hour lectures per week and 1 one-hour practice class per week. Total time commitment 170 hours Teaching period 25 July 2022 to 23 October 2022 Last self-enrol date 5 August 2022 Census date 31 August 2022 Last date to withdraw without fail 23 September 2022 Assessment period ends 18 November 2022 Semester 2 contact information
Last updated: 31 January 2024
Further information
- Texts
Last updated: 31 January 2024