Multivariate Statistics for Data Science (MAST90138)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

Availability
Semester 2
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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.
  • Evaluate and apply 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: 4 March 2025

Eligibility and requirements

Prerequisites

One of

Code Name Teaching period Credit Points
MAST30025 Linear Statistical Models
Semester 1 (On Campus - 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 (On Campus - Parkville)
 25

OR

Admission into the Statistical Data Science specialisation (formal) in the MC-DATASC Master of Data Science

OR

Admission into the Computational and Statistical Data Science specialisation (formal) in the MC-DATASC Master of Data Science

OR

Admission into one of:

• Master of Data Science (MC-DATASC) - Statistics Background Stream (pre- 2025)

• Master of Data Science (MC-DATASC) - Data Science Background Stream (pre-2025)

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: 4 March 2025

Assessment

DescriptionTimingPercentage

Up to 20 pages of written assignments (equivalent to approx. 20 hours)

  • 20 pages
From Week 6 to Week 820%

Up to 20 pages of written assignments (equivalent to approx. 20 hours)

  • 20 pages
From Week 11 to Week 1220%

Written examination

  • 3 hours
Hurdle requirement: Students must receive a mark of 50% or more for the written exam to pass the subject.		During the examination period60%

Last updated: 4 March 2025

Dates & times

  • Semester 2
    CoordinatorHeejung Shim
    Mode of deliveryOn Campus (Parkville)
    Contact hours36 hours comprising 2 one-hour lectures per week and 1 one-hour practice class per week.
    Total time commitment170 hours
    Teaching period28 July 2025 to 26 October 2025
    Last self-enrol date 8 August 2025
    Census date 1 September 2025
    Last date to withdraw without fail26 September 2025
    Assessment period ends21 November 2025

    Semester 2 contact information

    heejung.shim@unimelb.edu.au

What do these dates mean

Visit this webpage to find out about these key dates, including how they impact on:

  • Your tuition fees, academic transcript and statements.
  • And for Commonwealth Supported students, your:
       -  Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
     

    Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.

Last updated: 4 March 2025

Further information

  • Texts

    Prescribed texts

    There are no specifically prescribed or recommended texts for this subject.

  • Available through the Community Access Program

    About the Community Access Program (CAP)

    This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.

    Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants

    Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.

Last updated: 4 March 2025