Course Content
Part A. Data challenges (36 credit points)
You must complete the following units
Data challenges 1
Data challenges 2
Data challenges 3
Data challenges 4
Advanced data challenges
Part B. Techniques for data science (72 credit points)
Information technology units
36 credit points
Fundamentals of algorithms
Introduction to programming
Discrete mathematics for computer science
Modelling for data analysis
Data analytics
Advanced data analysis
Deep learning
CLAYTON: Mathematical science units
36 credit points
Continuous mathematics for computer science
Multivariate mathematics for data science
Introduction to computational mathematics
Mathematics of uncertainty
Mathematics of uncertainty (Advanced)
Random processes in the sciences and engineering
Computational linear algebra
Optimisation and operations research
MALAYSIA: Mathematical science units
36 credit points
Foundation mathematics
Engineering mathematics
Multivariate mathematics for data science
Introduction to computational mathematics
Computational linear algebra
Optimisation and operations research
Part C. Applied studies (24 credit points)
Level 1 and 2 units
Statistical methods for science
The nature and beauty of mathematics
Additional level 2 units
12 credit points
Differential equations with modelling
Introduction to computational mathematics
Mathematics of uncertainty
Mathematics of uncertainty (Advanced)
Mathematical statistics
Part D. Elective (12 credit points)
- This will enable you to further develop your technical skills or extend your knowledge in your selected applied studies. Alternatively, you can select units from across the university in which you are eligible to enrol.
