Multivariate Statistics for Data Science (MAST90138)

Graduate courseworkPoints: 12.5On Campus (Parkville)

You’re viewing the 2025 Handbook:
Or view archived Handbooks

Overview

Availability
Semester 2
FeesLook 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.
  • 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