Applications of Data Science

Postgraduate | 2026

Course page banner
area/catalogue icon
Area/Catalogue
INFO 5045
Course ID icon
Course ID
203977
Level of study
Level of study
Postgraduate
Unit value icon
Unit value
6
Course level icon
Course level
5
Study abroad and student exchange icon
Inbound study abroad and exchange
Inbound study abroad and exchange
The fee you pay will depend on the number and type of courses you study.
No
University-wide elective icon
University-wide elective course
No
Single course enrollment
Single course enrolment
No
alt
Note:
Course data is interim and subject to change

Course overview

A practical introduction to data modelling, analysis and prediction using contemporary software packages. An overview of common techniques and their implementation in software libraries. Selection of tools and techniques that are appropriate for different types and scale of data. Validation and interpretation of process outputs.

Course learning outcomes

  • Generate unsupervised and supervised methods to gain Information to bring about solutions
  • Formulate a range of regression methods as part of supervised learning
  • Formulate a range of classification methods as part of supervised learning
  • Construct tree methods to map non-linear relationships as part of supervised learning
  • Investigate the role of machine learning method in support of all the other techniques

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A