UO Predictive Analytics

Undergraduate | 2026

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Area/Catalogue
INFO 3018
Course ID icon
Course ID
203924
Level of study
Level of study
Undergraduate
Unit value icon
Unit value
6
Course level icon
Course level
3
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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.
Yes
University-wide elective icon
University-wide elective course
Yes
Single course enrollment
Single course enrolment
Yes
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Note:
Course data is interim and subject to change

Course overview

The aim of this course is to enable students to perform data exploration, visualisation, and analysis using predictive modelling techniques. The aim of this course is topics covered in this course include: classification: naive bayes, decision trees, SVM, Neural Networks, random forest, ensemble; regression: linear, logistic; model evaluation, overfitting, pruning.

Course learning outcomes

  • Develop accurate predictive models based on large data sets.
  • Perform predictive analytics on large data sets using an industry standard software tool-set.
  • Communicate appropriately with professional colleagues through visualisation and report.
  • Select and implement supervised machine learning techniques that are appropriate for the applications under consideration.
  • Select suitable model parameters for different supervised machine learning techniques.

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A