Data Science Practice

Undergraduate | 2026

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Area/Catalogue
STAT X301
Course ID icon
Course ID
208190
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

This course aims to equip students with the knowledge and skills to fit predictive models to real-life datasets. Building up the linear models and R coding learned in Statistical Practice, students will learn the general framework of predictive modelling both regression and classification, and how to implement the models in R, interpret the models and communicate to a general audience.

  • Workflows
  • Models
  • Models In The Real World

Course learning outcomes

  • Explain important factors and considerations for each key step of the data science workflow
  • Discriminate between predictive models with reference to the research question associated with a given dataset
  • Demonstrate an understanding the statistical underpinning of the chosen method
  • Safely implement data science methods
  • Interpret the results of data science methods
  • Communicate the output from the predictive models to a general audience

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A