1. Handbook
  2. Subjects
  3. Statistical Modelling for Data Science

Statistical Modelling for Data Science (MAST90139)

Graduate courseworkPoints: 12.5On Campus (Parkville)

You’re viewing the 2024 Handbook:
Or view archived Handbooks
You’re currently viewing the 2024 version of this subject

Overview

Availability
Semester 1
FeesLook up fees

Statistical models are central to data science applications. Modelling approaches such as linear and generalized linear models, mixed models, and non-parametric regression are developed. Applications to time series, longitudinal, and spatial data are discussed. Methods for causal inference and handling missing data are introduced.

Intended learning outcomes

On completion of this subject, students will be able to:

  • Understand the underlying statistical modelling framework and the limitations of such models.
  • Develop statistical models for data coming from diverse settings.
  • Appropriately handle data related issues such as missing and incomplete data in a rigorous and justifiable manner.
  • Understand how to model dependencies in data such as those that occur in time and spatial domains.
  • Pursue further studies in this and related areas, or to be work ready as an applied statistician or a data scientist.

Generic skills

In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include:

  • 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; and
  • Time-management skills: the ability to meet regular deadlines while balancing competing commitments.

Last updated: 8 November 2024