Topics in Data Science B

Undergraduate

Course page banner
Mode icon
Mode
Mode
Your studies will be on-campus, and may include some online delivery
On campus
area/catalogue icon
Area/Catalogue
STAT 4002
Course ID icon
Course ID
200060
Campus icon
Campus
Mawson Lakes, Adelaide City Campus East
Level of study
Level of study
Undergraduate
Unit value icon
Unit value
6
Course owner
Course owner
School of Mathematical Science
Course level icon
Course level
4
Work Integrated Learning course
Work Integrated Learning course
No
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

Course overview

In 2026, the course focuses on Data Science for Molecular Biology and Evolution. It covers a range of statistical and machine learning techniques for the analysis of genomics and other biological data. Topics include exploratory data analysis of high-dimensional genomics datasets; stochastic modelling and inference methods applied to epidemiology, immunology, and evolutionary biology; and recent advances in the application of AI to genomics data analysis.

Course learning outcomes

  • Apply research skills to gain new knowledge in a topic of data science
  • Demonstrate knowledge of advanced concepts, theorems and techniques of data science, applied statistics or analysis
  • Apply these mathematical, computational and statistical techniques to real-world situations
  • Communicate effectively to clearly describe the process and/or results

Prerequisite(s)

  • Must have completed 144 units towards the HMATH OR completed Grad Dip in Mathematical Sciences AND must have completed 18 units of level 3 data science or statistics courses

Corequisite(s)

N/A

Antirequisite(s)

N/A

Degree list
The following degrees include this course