Why Bachelor of Mathematics (Honours)?
Harness data to answer complex questions.
On its own, data doesn’t help us make decisions. We need people with the skills to analyse and make sense of it. In this way, data scientists are the ultimate detectives. They specialise in asking sharp questions and unearthing useful information from large and complex datasets. Think global population, climate, satellite imaging or financial market data.
Their expertise is key in identifying patterns and insights that can then be used to inform decision making in areas like public health, education, pharmaceutical, insurance, manufacturing and more.
Build a skillset that’s transferable, in-demand and vital to all business sectors.
Overview
Discover how to extract useful insights from large and complex datasets in our Bachelor of Mathematics (Honours).
This degree provides you with the same breadth and depth of learning as the foundation bachelor degree, but with the additional opportunity to advance directly into a research-focused honours year.
The Data Science major equips you with the knowledge and skills to make sense of large, complicated datasets to extract relevant insights and apply these across a range of disciplines and situations. Build deep knowledge of the abstract theories used in mathematics and statistical modelling. Learn how to apply fundamental concepts in calculus, algebra, statistics and data taming.
Deeply practical, you’ll have multiple opportunities throughout to hone your technical and professional skills – whether that’s developing maths-driven solutions for real business problems in our Maths Clinic, completing a capstone industry project or industry placement.
Set yourself apart to future employers by undertaking a major honours research project on a topic of your choosing. This experience also equips you with a solid foundation upon which to pursue further research through a PhD.
Graduate career-ready and with the confidence to apply your knowledge in highly rewarding fields of interest like finance, healthcare, technology, education, government and more.
Key features
Build knowledge in evidence-based methods of collecting, modelling and analysing data.- Gain understanding of the core theories, principles and techniques that underpin the mathematical sciences.
- Explore topics in artificial intelligence, machine learning and natural language processing models.
- Use industry-standard tools and software to perform statistical analysis.
- Hone your professional skills through our specialised Maths Clinic, an industry internship or supervised project.
- Graduate with a competitive edge by completing a major honours research project.
What you'll learn
In the Bachelor of Mathematics (Honours) majoring in Data Science, you will complete a range of core courses that provide a solid grounding in key mathematical and statistical concepts, theories, principles and methods.
In your first year, you’ll be introduced to several key, foundational mathematical concepts and principles. You’ll take core classes in linear algebra, calculus, probability and statistics, problem-solving and programming. Additionally, you’ll have the option to broaden your focus through the choice of electives in areas such as discrete mathematics, critical evaluation in data science, geometry and others.
In second year, you’ll tackle advanced topics in statistical practice, multivariable calculus, differential equations and algebra. –also begin to take statistics-focused courses. You’ll also complete two elective courses, allowing you to specialise further in areas of interest.
You’ll continue to dive deeper into data science-driven courses in third and fourth year. Learning how to model complex networks, use computational statistics and apply decision science principles. You’ll also discover how to perform relevant analyses using new and advanced tools and algorithms by applying these to complex and everyday problems.
Third year includes a strong focus on the development and application of professional skills. To help you hone your professional skillset, you’ll have the option to undertake an industry focused experience through completing one of the following: a Maths Clinic experience, internship in mathematics or industry-focused mathematics project.
In your final year, you’ll complete a major honours research project on a topic of your choosing. Under the guidance of our supportive academics, you’ll develop high-level research skills and produce a final project that contributes new knowledge to the field.
Together, these experiences ensure you’ll graduate with the knowledge, skills and expertise to thrive in your data science career.
Majors
The Bachelor of Mathematics (Honours) is also available with majors in the following:
What courses you'll study
Complete 192 units comprising:
- 84 units for Core courses, and
- 18 units from Work integrated learning, and
- Either:
- 48 units for Major, or
- 48 units for Discipline courses, and
- 42 units for Electives
Complete 84 units comprising:
- 18 units from Common core, and
- 66 units for all Program core
| Course name | Course code | Units | |
|---|---|---|---|
|
Course name
An Ethically Rich Life
|
Course code
COREX001
|
Units
6
|
|
|
Course name
Fact or Fiction: Data for Everyone
|
Course code
COREX002
|
Units
6
|
|
|
Course name
Igniting Change: Ideas to Action
|
Course code
COREX003
|
Units
6
|
|
|
Course name
Proppa Ways, Future Practice
|
Course code
COREX004
|
Units
6
|
|
|
Course name
Responsible AI: Bridging Ethics, Education and Industry
|
Course code
COREX005
|
Units
6
|
|
|
Course name
Ways of Being, Ways of Seeing
|
Course code
COREX006
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Problem Solving and Programming
|
Course code
COMP1002
|
Units
6
|
|
|
Course name
Calculus 1
|
Course code
MATH1004
|
Units
6
|
|
|
Course name
Calculus 2
|
Course code
MATH1005
|
Units
6
|
|
|
Course name
Communication and Research Skills in Mathematics
|
Course code
MATH1020
|
Units
6
|
|
|
Course name
Linear Algebra
|
Course code
MATHX104
|
Units
6
|
|
|
Course name
Multivariable Calculus
|
Course code
MATHX203
|
Units
6
|
|
|
Course name
Mathematics Research Project 1
|
Course code
MATHX406
|
Units
12
|
|
|
Course name
Mathematics Research Project 2
|
Course code
MATHX407
|
Units
12
|
|
|
Course name
Probability and Statistics
|
Course code
STATX100
|
Units
6
|
Notes
Program core - For students completing the STATHMATH - Statistics or the PMTHHMATH - Pure Mathematics majors, Calculus 1 will not be compulsory for students entering the program who have successfully completed SACE Stage 2 Specialist Mathematics (or equivalent). For these students, Calculus 1 can be replaced with any first year mathematics elective.
Complete 48 units comprising:
- 18 units for all Data Science major courses, and
- 6 units from Level 1 Data Science major courses, and
- 6 units from Level 2 Data Science major courses, and
- 18 units from Level 3 Data Science major courses
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Optimisation
|
Course code
MATHX205
|
Units
6
|
|
|
Course name
Probability
|
Course code
STATX200
|
Units
6
|
|
|
Course name
Data Science Practice
|
Course code
STATX301
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Discrete Mathematics
|
Course code
MATH1006
|
Units
6
|
|
|
Course name
Geometry
|
Course code
MATH1007
|
Units
6
|
|
|
Course name
First Steps in Mathematics Research
|
Course code
MATH1011
|
Units
6
|
|
|
Course name
Mathematics in Action
|
Course code
MATH1012
|
Units
6
|
|
|
Course name
Introduction to Mathematical Data Science
|
Course code
MATH1035
|
Units
6
|
|
|
Course name
Critical Evaluation in Data Science
|
Course code
MATHX106
|
Units
6
|
|
|
Course name
Information Technology Systems
|
Course code
INFO1012
|
Units
6
|
|
|
Course name
Structured Data
|
Course code
COMP1003
|
Units
6
|
|
|
Course name
Object-Oriented Programming
|
Course code
COMP1005
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Algebra
|
Course code
MATH2007
|
Units
6
|
|
|
Course name
Statistical Theory
|
Course code
STAT2003
|
Units
6
|
|
|
Course name
Introduction to Networks
|
Course code
MATHX102
|
Units
6
|
|
|
Course name
Mathematical Modelling
|
Course code
MATHX103
|
Units
6
|
|
|
Course name
Differential Equations
|
Course code
MATHX202
|
Units
6
|
|
|
Course name
Data Visualisation
|
Course code
MATHX314
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Computational Statistics
|
Course code
STAT3006
|
Units
6
|
|
|
Course name
Text and Social Media Analytics
|
Course code
COMPX300
|
Units
6
|
|
|
Course name
Simulation for Decision Science
|
Course code
MATHX207
|
Units
6
|
|
|
Course name
Statistical Methodology
|
Course code
STATX302
|
Units
6
|
|
|
Course name
Advanced Optimisation
|
Course code
MATHX302
|
Units
6
|
|
|
Course name
Complex Networks
|
Course code
MATHX303
|
Units
6
|
|
|
Course name
Time Series Analysis
|
Course code
MATHX313
|
Units
6
|
Complete exactly 18 units from the following:
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Professional Practice
|
Course code
MATH1021
|
Units
6
|
|
|
Course name
Statistical Practice
|
Course code
STATX290
|
Units
6
|
|
|
Course name
Mathematics Clinic 1
|
Course code
MATH3013
|
Units
6
|
|
|
Course name
Mathematics Clinic 2
|
Course code
MATH3900
|
Units
6
|
|
|
Course name
Project in Mathematics
|
Course code
MATH3901
|
Units
6
|
|
|
Course name
Internship in Mathematics
|
Course code
MATHX101
|
Units
6
|
Complete 42 units comprising:
- 6 units from University-wide electives Level 1, and
- One of the following:
- 6 units from Discipline electives Level 2, or
- 6 units from Applied Mathematics electives Level 2, or
- 6 units from Data Science electives Level 2, or
- 6 units from Pure Mathematics electives Level 2, or
- 6 units from Statistics electives Level 2, and
- One of the following:
- 6 units from Discipline electives Level 3, or
- 6 units from Applied Mathematics electives Level 3, or
- 6 units from Data Science electives Level 3, or
- 6 units from Pure Mathematics electives Level 3, or
- 6 units from Statistics electives Level 3, and
- One of the following:
- 24 units from Discipline electives Level 4, or
- 24 units from Applied Mathematics electives Level 4, or
- 24 units from Data Science electives Level 4, or
- 24 units from Pure Mathematics electives Level 4, or
- 24 units from Statistics electives Level 4
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Elective 1
|
Course code
AUXX1011
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Elective 1
|
Course code
AUXX1011
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Further Steps in Mathematics Research
|
Course code
MATH2010
|
Units
6
|
|
|
Course name
Statistical Theory
|
Course code
STAT2003
|
Units
6
|
|
|
Course name
Complex Analysis
|
Course code
MATHX100
|
Units
6
|
|
|
Course name
Introduction to Networks
|
Course code
MATHX102
|
Units
6
|
|
|
Course name
Mathematical Modelling
|
Course code
MATHX103
|
Units
6
|
|
|
Course name
Numerical Methods
|
Course code
MATHX204
|
Units
6
|
|
|
Course name
Data Visualisation
|
Course code
MATHX314
|
Units
6
|
|
|
Course name
Algebra
|
Course code
MATHX201
|
Units
6
|
|
|
Course name
Differential Equations
|
Course code
MATHX202
|
Units
6
|
|
|
Course name
Optimisation
|
Course code
MATHX205
|
Units
6
|
|
|
Course name
Real Analysis
|
Course code
MATHX206
|
Units
6
|
|
|
Course name
Probability
|
Course code
STATX200
|
Units
6
|
|
|
Course name
Design of Experiments
|
Course code
STATX500
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Further Steps in Mathematics Research
|
Course code
MATH2010
|
Units
6
|
|
|
Course name
Statistical Theory
|
Course code
STAT2003
|
Units
6
|
|
|
Course name
Data Visualisation
|
Course code
MATHX314
|
Units
6
|
|
|
Course name
Complex Analysis
|
Course code
MATHX100
|
Units
6
|
|
|
Course name
Introduction to Networks
|
Course code
MATHX102
|
Units
6
|
|
|
Course name
Mathematical Modelling
|
Course code
MATHX103
|
Units
6
|
|
|
Course name
Algebra
|
Course code
MATHX201
|
Units
6
|
|
|
Course name
Differential Equations
|
Course code
MATHX202
|
Units
6
|
|
|
Course name
Numerical Methods
|
Course code
MATHX204
|
Units
6
|
|
|
Course name
Real Analysis
|
Course code
MATHX206
|
Units
6
|
|
|
Course name
Design of Experiments
|
Course code
STATX500
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Further Steps in Mathematics Research
|
Course code
MATH2010
|
Units
6
|
|
|
Course name
Statistical Theory
|
Course code
STAT2003
|
Units
6
|
|
|
Course name
Complex Analysis
|
Course code
MATHX100
|
Units
6
|
|
|
Course name
Mathematical Modelling
|
Course code
MATHX103
|
Units
6
|
|
|
Course name
Introduction to Networks
|
Course code
MATHX102
|
Units
6
|
|
|
Course name
Differential Equations
|
Course code
MATHX202
|
Units
6
|
|
|
Course name
Numerical Methods
|
Course code
MATHX204
|
Units
6
|
|
|
Course name
Optimisation
|
Course code
MATHX205
|
Units
6
|
|
|
Course name
Data Visualisation
|
Course code
MATHX314
|
Units
6
|
|
|
Course name
Probability
|
Course code
STATX200
|
Units
6
|
|
|
Course name
Design of Experiments
|
Course code
STATX500
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Further Steps in Mathematics Research
|
Course code
MATH2010
|
Units
6
|
|
|
Course name
Complex Analysis
|
Course code
MATHX100
|
Units
6
|
|
|
Course name
Introduction to Networks
|
Course code
MATHX102
|
Units
6
|
|
|
Course name
Mathematical Modelling
|
Course code
MATHX103
|
Units
6
|
|
|
Course name
Algebra
|
Course code
MATHX201
|
Units
6
|
|
|
Course name
Differential Equations
|
Course code
MATHX202
|
Units
6
|
|
|
Course name
Numerical Methods
|
Course code
MATHX204
|
Units
6
|
|
|
Course name
Optimisation
|
Course code
MATHX205
|
Units
6
|
|
|
Course name
Real Analysis
|
Course code
MATHX206
|
Units
6
|
|
|
Course name
Data Visualisation
|
Course code
MATHX314
|
Units
6
|
|
|
Course name
Design of Experiments
|
Course code
STATX500
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Elective 1
|
Course code
AUXX1011
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Cryptography
|
Course code
MATH3022
|
Units
6
|
|
|
Course name
Advanced Statistical Theory
|
Course code
STAT3005
|
Units
6
|
|
|
Course name
Computational Statistics
|
Course code
STAT3006
|
Units
6
|
|
|
Course name
Text and Social Media Analytics
|
Course code
COMPX300
|
Units
6
|
|
|
Course name
Simulation for Decision Science
|
Course code
MATHX207
|
Units
6
|
|
|
Course name
Advanced Differential Equations
|
Course code
MATHX300
|
Units
6
|
|
|
Course name
Advanced Mathematical Modelling
|
Course code
MATHX301
|
Units
6
|
|
|
Course name
Advanced Optimisation
|
Course code
MATHX302
|
Units
6
|
|
|
Course name
Complex Networks
|
Course code
MATHX303
|
Units
6
|
|
|
Course name
Fluid Dynamics
|
Course code
MATHX304
|
Units
6
|
|
|
Course name
Functional Analysis
|
Course code
MATHX305
|
Units
6
|
|
|
Course name
Geometry of Surfaces
|
Course code
MATHX306
|
Units
6
|
|
|
Course name
Groups, Rings and Fields
|
Course code
MATHX307
|
Units
6
|
|
|
Course name
Introduction to Topological Data Analysis
|
Course code
MATHX308
|
Units
6
|
|
|
Course name
Mathematical Biology
|
Course code
MATHX309
|
Units
6
|
|
|
Course name
Measure and Integration
|
Course code
MATHX310
|
Units
6
|
|
|
Course name
Metric and Topological Spaces
|
Course code
MATHX311
|
Units
6
|
|
|
Course name
Number Theory
|
Course code
MATHX312
|
Units
6
|
|
|
Course name
Time Series Analysis
|
Course code
MATHX313
|
Units
6
|
|
|
Course name
Bayesian Statistical Practice
|
Course code
STATX300
|
Units
6
|
|
|
Course name
Data Science Practice
|
Course code
STATX301
|
Units
6
|
|
|
Course name
Statistical Methodology
|
Course code
STATX302
|
Units
6
|
|
|
Course name
Stochastic Processes
|
Course code
STATX303
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Simulation for Decision Science
|
Course code
MATH2000
|
Units
6
|
|
|
Course name
Cryptography
|
Course code
MATH3022
|
Units
6
|
|
|
Course name
Stochastic Processes
|
Course code
STAT3002
|
Units
6
|
|
|
Course name
Advanced Statistical Theory
|
Course code
STAT3005
|
Units
6
|
|
|
Course name
Computational Statistics
|
Course code
STAT3006
|
Units
6
|
|
|
Course name
Text and Social Media Analytics
|
Course code
COMPX300
|
Units
6
|
|
|
Course name
Advanced Differential Equations
|
Course code
MATHX300
|
Units
6
|
|
|
Course name
Advanced Mathematical Modelling
|
Course code
MATHX301
|
Units
6
|
|
|
Course name
Advanced Optimisation
|
Course code
MATHX302
|
Units
6
|
|
|
Course name
Complex Networks
|
Course code
MATHX303
|
Units
6
|
|
|
Course name
Fluid Dynamics
|
Course code
MATHX304
|
Units
6
|
|
|
Course name
Functional Analysis
|
Course code
MATHX305
|
Units
6
|
|
|
Course name
Geometry of Surfaces
|
Course code
MATHX306
|
Units
6
|
|
|
Course name
Groups, Rings and Fields
|
Course code
MATHX307
|
Units
6
|
|
|
Course name
Introduction to Topological Data Analysis
|
Course code
MATHX308
|
Units
6
|
|
|
Course name
Mathematical Biology
|
Course code
MATHX309
|
Units
6
|
|
|
Course name
Measure and Integration
|
Course code
MATHX310
|
Units
6
|
|
|
Course name
Metric and Topological Spaces
|
Course code
MATHX311
|
Units
6
|
|
|
Course name
Number Theory
|
Course code
MATHX312
|
Units
6
|
|
|
Course name
Time Series Analysis
|
Course code
MATHX313
|
Units
6
|
|
|
Course name
Bayesian Statistical Practice
|
Course code
STATX300
|
Units
6
|
|
|
Course name
Data Science Practice
|
Course code
STATX301
|
Units
6
|
|
|
Course name
Statistical Methodology
|
Course code
STATX302
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Simulation for Decision Science
|
Course code
MATH2000
|
Units
6
|
|
|
Course name
Cryptography
|
Course code
MATH3022
|
Units
6
|
|
|
Course name
Stochastic Processes
|
Course code
STAT3002
|
Units
6
|
|
|
Course name
Advanced Statistical Theory
|
Course code
STAT3005
|
Units
6
|
|
|
Course name
Computational Statistics
|
Course code
STAT3006
|
Units
6
|
|
|
Course name
Text and Social Media Analytics
|
Course code
COMPX300
|
Units
6
|
|
|
Course name
Advanced Mathematical Modelling
|
Course code
MATHX301
|
Units
6
|
|
|
Course name
Advanced Differential Equations
|
Course code
MATHX300
|
Units
6
|
|
|
Course name
Advanced Optimisation
|
Course code
MATHX302
|
Units
6
|
|
|
Course name
Complex Networks
|
Course code
MATHX303
|
Units
6
|
|
|
Course name
Fluid Dynamics
|
Course code
MATHX304
|
Units
6
|
|
|
Course name
Functional Analysis
|
Course code
MATHX305
|
Units
6
|
|
|
Course name
Geometry of Surfaces
|
Course code
MATHX306
|
Units
6
|
|
|
Course name
Groups, Rings and Fields
|
Course code
MATHX307
|
Units
6
|
|
|
Course name
Introduction to Topological Data Analysis
|
Course code
MATHX308
|
Units
6
|
|
|
Course name
Mathematical Biology
|
Course code
MATHX309
|
Units
6
|
|
|
Course name
Measure and Integration
|
Course code
MATHX310
|
Units
6
|
|
|
Course name
Metric and Topological Spaces
|
Course code
MATHX311
|
Units
6
|
|
|
Course name
Number Theory
|
Course code
MATHX312
|
Units
6
|
|
|
Course name
Time Series Analysis
|
Course code
MATHX313
|
Units
6
|
|
|
Course name
Bayesian Statistical Practice
|
Course code
STATX300
|
Units
6
|
|
|
Course name
Data Science Practice
|
Course code
STATX301
|
Units
6
|
|
|
Course name
Statistical Methodology
|
Course code
STATX302
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Cryptography
|
Course code
MATH3022
|
Units
6
|
|
|
Course name
Stochastic Processes
|
Course code
STAT3002
|
Units
6
|
|
|
Course name
Advanced Statistical Theory
|
Course code
STAT3005
|
Units
6
|
|
|
Course name
Computational Statistics
|
Course code
STAT3006
|
Units
6
|
|
|
Course name
Text and Social Media Analytics
|
Course code
COMPX300
|
Units
6
|
|
|
Course name
Advanced Differential Equations
|
Course code
MATHX300
|
Units
6
|
|
|
Course name
Advanced Mathematical Modelling
|
Course code
MATHX301
|
Units
6
|
|
|
Course name
Advanced Optimisation
|
Course code
MATHX302
|
Units
6
|
|
|
Course name
Complex Networks
|
Course code
MATHX303
|
Units
6
|
|
|
Course name
Fluid Dynamics
|
Course code
MATHX304
|
Units
6
|
|
|
Course name
Functional Analysis
|
Course code
MATHX305
|
Units
6
|
|
|
Course name
Geometry of Surfaces
|
Course code
MATHX306
|
Units
6
|
|
|
Course name
Groups, Rings and Fields
|
Course code
MATHX307
|
Units
6
|
|
|
Course name
Introduction to Topological Data Analysis
|
Course code
MATHX308
|
Units
6
|
|
|
Course name
Mathematical Biology
|
Course code
MATHX309
|
Units
6
|
|
|
Course name
Measure and Integration
|
Course code
MATHX310
|
Units
6
|
|
|
Course name
Metric and Topological Spaces
|
Course code
MATHX311
|
Units
6
|
|
|
Course name
Number Theory
|
Course code
MATHX312
|
Units
6
|
|
|
Course name
Time Series Analysis
|
Course code
MATHX313
|
Units
6
|
|
|
Course name
Bayesian Statistical Practice
|
Course code
STATX300
|
Units
6
|
|
|
Course name
Data Science Practice
|
Course code
STATX301
|
Units
6
|
|
|
Course name
Simulation for Decision Science
|
Course code
MATHX207
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Mathematical Generative Artificial Intelligence
|
Course code
ARTI1000
|
Units
6
|
|
|
Course name
Information Theory
|
Course code
COMP4007
|
Units
6
|
|
|
Course name
Statistical Machine Learning
|
Course code
MATH4008
|
Units
6
|
|
|
Course name
Algebraic Topology
|
Course code
MATH4010
|
Units
6
|
|
|
Course name
Reinforcement Learning
|
Course code
MATH6019
|
Units
6
|
|
|
Course name
Bayesian Statistical Theory
|
Course code
STAT3003
|
Units
6
|
|
|
Course name
Topics in Data Science A
|
Course code
STAT4001
|
Units
6
|
|
|
Course name
Topics in Data Science B
|
Course code
STAT4002
|
Units
6
|
|
|
Course name
Advanced Stochastic Processes
|
Course code
MATHX200
|
Units
6
|
|
|
Course name
Advanced Time Series
|
Course code
MATHX400
|
Units
6
|
|
|
Course name
Asymptotic Methods
|
Course code
MATHX401
|
Units
6
|
|
|
Course name
Category Theory
|
Course code
MATHX402
|
Units
6
|
|
|
Course name
Integral Transforms
|
Course code
MATHX403
|
Units
6
|
|
|
Course name
Lie Algebras and Lie Groups
|
Course code
MATHX404
|
Units
6
|
|
|
Course name
Mathematics of Artificial Intelligence
|
Course code
MATHX405
|
Units
6
|
|
|
Course name
Riemannian Geometry
|
Course code
MATHX408
|
Units
6
|
|
|
Course name
Smooth Manifolds
|
Course code
MATHX409
|
Units
6
|
|
|
Course name
Topics in Applied Mathematics A
|
Course code
MATHX410
|
Units
6
|
|
|
Course name
Topics in Applied Mathematics B
|
Course code
MATHX411
|
Units
6
|
|
|
Course name
Topics in Pure Mathematics A
|
Course code
MATHX412
|
Units
6
|
|
|
Course name
Topics in Pure Mathematics B
|
Course code
MATHX413
|
Units
6
|
|
|
Course name
Natural Language Processing
|
Course code
MATHX415
|
Units
6
|
|
|
Course name
Predictive Analytics
|
Course code
STATX400
|
Units
6
|
|
|
Course name
Spatial Statistics
|
Course code
STATX401
|
Units
6
|
|
|
Course name
Survey Statistics
|
Course code
STATX402
|
Units
6
|
|
|
Course name
Causal Inference
|
Course code
STATX403
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Information Theory
|
Course code
COMP4007
|
Units
6
|
|
|
Course name
Statistical Machine Learning
|
Course code
MATH4008
|
Units
6
|
|
|
Course name
Algebraic Topology
|
Course code
MATH4010
|
Units
6
|
|
|
Course name
Reinforcement Learning
|
Course code
MATH6019
|
Units
6
|
|
|
Course name
Bayesian Statistical Theory
|
Course code
STAT3003
|
Units
6
|
|
|
Course name
Topics in Data Science A
|
Course code
STAT4001
|
Units
6
|
|
|
Course name
Topics in Data Science B
|
Course code
STAT4002
|
Units
6
|
|
|
Course name
Advanced Stochastic Processes
|
Course code
MATHX200
|
Units
6
|
|
|
Course name
Advanced Time Series
|
Course code
MATHX400
|
Units
6
|
|
|
Course name
Asymptotic Methods
|
Course code
MATHX401
|
Units
6
|
|
|
Course name
Category Theory
|
Course code
MATHX402
|
Units
6
|
|
|
Course name
Integral Transforms
|
Course code
MATHX403
|
Units
6
|
|
|
Course name
Lie Algebras and Lie Groups
|
Course code
MATHX404
|
Units
6
|
|
|
Course name
Mathematics of Artificial Intelligence
|
Course code
MATHX405
|
Units
6
|
|
|
Course name
Riemannian Geometry
|
Course code
MATHX408
|
Units
6
|
|
|
Course name
Smooth Manifolds
|
Course code
MATHX409
|
Units
6
|
|
|
Course name
Topics in Applied Mathematics A
|
Course code
MATHX410
|
Units
6
|
|
|
Course name
Topics in Applied Mathematics B
|
Course code
MATHX411
|
Units
6
|
|
|
Course name
Topics in Pure Mathematics A
|
Course code
MATHX412
|
Units
6
|
|
|
Course name
Topics in Pure Mathematics B
|
Course code
MATHX413
|
Units
6
|
|
|
Course name
Natural Language Processing
|
Course code
MATHX415
|
Units
6
|
|
|
Course name
Predictive Analytics
|
Course code
STATX400
|
Units
6
|
|
|
Course name
Spatial Statistics
|
Course code
STATX401
|
Units
6
|
|
|
Course name
Survey Statistics
|
Course code
STATX402
|
Units
6
|
|
|
Course name
Causal Inference
|
Course code
STATX403
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Mathematical Generative Artificial Intelligence
|
Course code
ARTI1000
|
Units
6
|
|
|
Course name
Information Theory
|
Course code
COMP4007
|
Units
6
|
|
|
Course name
Advanced Stochastic Processes
|
Course code
MATH2002
|
Units
6
|
|
|
Course name
Statistical Machine Learning
|
Course code
MATH4008
|
Units
6
|
|
|
Course name
Algebraic Topology
|
Course code
MATH4010
|
Units
6
|
|
|
Course name
Reinforcement Learning
|
Course code
MATH6019
|
Units
6
|
|
|
Course name
Bayesian Statistical Theory
|
Course code
STAT3003
|
Units
6
|
|
|
Course name
Topics in Data Science A
|
Course code
STAT4001
|
Units
6
|
|
|
Course name
Topics in Data Science B
|
Course code
STAT4002
|
Units
6
|
|
|
Course name
Advanced Time Series
|
Course code
MATHX400
|
Units
6
|
|
|
Course name
Asymptotic Methods
|
Course code
MATHX401
|
Units
6
|
|
|
Course name
Category Theory
|
Course code
MATHX402
|
Units
6
|
|
|
Course name
Integral Transforms
|
Course code
MATHX403
|
Units
6
|
|
|
Course name
Lie Algebras and Lie Groups
|
Course code
MATHX404
|
Units
6
|
|
|
Course name
Mathematics of Artificial Intelligence
|
Course code
MATHX405
|
Units
6
|
|
|
Course name
Riemannian Geometry
|
Course code
MATHX408
|
Units
6
|
|
|
Course name
Smooth Manifolds
|
Course code
MATHX409
|
Units
6
|
|
|
Course name
Topics in Applied Mathematics A
|
Course code
MATHX410
|
Units
6
|
|
|
Course name
Topics in Applied Mathematics B
|
Course code
MATHX411
|
Units
6
|
|
|
Course name
Topics in Pure Mathematics A
|
Course code
MATHX412
|
Units
6
|
|
|
Course name
Topics in Pure Mathematics B
|
Course code
MATHX413
|
Units
6
|
|
|
Course name
Natural Language Processing
|
Course code
MATHX415
|
Units
6
|
|
|
Course name
Predictive Analytics
|
Course code
STATX400
|
Units
6
|
|
|
Course name
Spatial Statistics
|
Course code
STATX401
|
Units
6
|
|
|
Course name
Survey Statistics
|
Course code
STATX402
|
Units
6
|
|
|
Course name
Causal Inference
|
Course code
STATX403
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Information Theory
|
Course code
COMP4007
|
Units
6
|
|
|
Course name
Statistical Machine Learning
|
Course code
MATH4008
|
Units
6
|
|
|
Course name
Algebraic Topology
|
Course code
MATH4010
|
Units
6
|
|
|
Course name
Reinforcement Learning
|
Course code
MATH6019
|
Units
6
|
|
|
Course name
Bayesian Statistical Theory
|
Course code
STAT3003
|
Units
6
|
|
|
Course name
Topics in Data Science A
|
Course code
STAT4001
|
Units
6
|
|
|
Course name
Topics in Data Science B
|
Course code
STAT4002
|
Units
6
|
|
|
Course name
Advanced Stochastic Processes
|
Course code
MATHX200
|
Units
6
|
|
|
Course name
Advanced Time Series
|
Course code
MATHX400
|
Units
6
|
|
|
Course name
Asymptotic Methods
|
Course code
MATHX401
|
Units
6
|
|
|
Course name
Category Theory
|
Course code
MATHX402
|
Units
6
|
|
|
Course name
Integral Transforms
|
Course code
MATHX403
|
Units
6
|
|
|
Course name
Lie Algebras and Lie Groups
|
Course code
MATHX404
|
Units
6
|
|
|
Course name
Mathematics of Artificial Intelligence
|
Course code
MATHX405
|
Units
6
|
|
|
Course name
Riemannian Geometry
|
Course code
MATHX408
|
Units
6
|
|
|
Course name
Smooth Manifolds
|
Course code
MATHX409
|
Units
6
|
|
|
Course name
Topics in Applied Mathematics A
|
Course code
MATHX410
|
Units
6
|
|
|
Course name
Topics in Applied Mathematics B
|
Course code
MATHX411
|
Units
6
|
|
|
Course name
Topics in Pure Mathematics A
|
Course code
MATHX412
|
Units
6
|
|
|
Course name
Topics in Pure Mathematics B
|
Course code
MATHX413
|
Units
6
|
|
|
Course name
Natural Language Processing
|
Course code
MATHX415
|
Units
6
|
|
|
Course name
Predictive Analytics
|
Course code
STATX400
|
Units
6
|
|
|
Course name
Spatial Statistics
|
Course code
STATX401
|
Units
6
|
|
|
Course name
Survey Statistics
|
Course code
STATX402
|
Units
6
|
|
|
Course name
Causal Inference
|
Course code
STATX403
|
Units
6
|
| Course name | Course code | Units | |
|---|---|---|---|
|
|
Course name
Mathematical Generative Artificial Intelligence
|
Course code
ARTI1000
|
Units
6
|
|
|
Course name
Information Theory
|
Course code
COMP4007
|
Units
6
|
|
|
Course name
Statistical Machine Learning
|
Course code
MATH4008
|
Units
6
|
|
|
Course name
Algebraic Topology
|
Course code
MATH4010
|
Units
6
|
|
|
Course name
Reinforcement Learning
|
Course code
MATH6019
|
Units
6
|
|
|
Course name
Bayesian Statistical Theory
|
Course code
STAT3003
|
Units
6
|
|
|
Course name
Topics in Data Science A
|
Course code
STAT4001
|
Units
6
|
|
|
Course name
Topics in Data Science B
|
Course code
STAT4002
|
Units
6
|
|
|
Course name
Advanced Stochastic Processes
|
Course code
MATHX200
|
Units
6
|
|
|
Course name
Advanced Time Series
|
Course code
MATHX400
|
Units
6
|
|
|
Course name
Asymptotic Methods
|
Course code
MATHX401
|
Units
6
|
|
|
Course name
Category Theory
|
Course code
MATHX402
|
Units
6
|
|
|
Course name
Integral Transforms
|
Course code
MATHX403
|
Units
6
|
|
|
Course name
Lie Algebras and Lie Groups
|
Course code
MATHX404
|
Units
6
|
|
|
Course name
Mathematics of Artificial Intelligence
|
Course code
MATHX405
|
Units
6
|
|
|
Course name
Riemannian Geometry
|
Course code
MATHX408
|
Units
6
|
|
|
Course name
Smooth Manifolds
|
Course code
MATHX409
|
Units
6
|
|
|
Course name
Topics in Applied Mathematics A
|
Course code
MATHX410
|
Units
6
|
|
|
Course name
Topics in Applied Mathematics B
|
Course code
MATHX411
|
Units
6
|
|
|
Course name
Topics in Pure Mathematics A
|
Course code
MATHX412
|
Units
6
|
|
|
Course name
Topics in Pure Mathematics B
|
Course code
MATHX413
|
Units
6
|
|
|
Course name
Natural Language Processing
|
Course code
MATHX415
|
Units
6
|
|
|
Course name
Predictive Analytics
|
Course code
STATX400
|
Units
6
|
|
|
Course name
Spatial Statistics
|
Course code
STATX401
|
Units
6
|
|
|
Course name
Survey Statistics
|
Course code
STATX402
|
Units
6
|
|
|
Course name
Causal Inference
|
Course code
STATX403
|
Units
6
|
Notes
Discipline electives Level 2 - Students may elect to replace the Discipline Electives with open electives under guidance from the Program Director
Applied Mathematics electives Level 2 - Students may elect to replace the Discipline Electives with open electives under guidance from the Program Director
Data Science electives Level 2 - Students may elect to replace the Discipline Electives with open electives under guidance from the Program Director
Pure Mathematics electives Level 2 - Students may elect to replace the Discipline Electives with open electives under guidance from the Program Director
Statistics electives Level 2 - Students may elect to replace the Discipline Electives with open electives under guidance from the Program Director
Discipline electives Level 3 - Students may elect to replace the Discipline Electives with open electives under guidance from the Program Director
Applied Mathematics electives Level 3 - Students may elect to replace the Discipline Electives with open electives under guidance from the Program Director
Data Science electives Level 3 - Students may elect to replace the Discipline Electives with open electives under guidance from the Program Director
Pure Mathematics electives Level 3 - Students may elect to replace the Discipline Electives with open electives under guidance from the Program Director
Statistics electives Level 3 - Students may elect to replace the Discipline Electives with open electives under guidance from the Program Director
Applied Mathematics electives Level 4 - It is recommended that students consult with the program director prior to enrolling in fourth year.
Data Science electives Level 4 - It is recommended that students consult with the program director prior to enrolling in fourth year.
Pure Mathematics electives Level 4 - It is recommended that students consult with the program director prior to enrolling in fourth year.
Statistics electives Level 4 - It is recommended that students consult with the program director prior to enrolling in fourth year.
Career outcomes
Graduates of this degree will emerge with the high-level statistical knowledge and skills necessary for success in a wide range of data science-driven roles. Highly sought-after for their advanced technical skills in mathematical modelling and statistical analysis, graduates are equipped for careers in finance and banking, business, technology, healthcare, engineering, government policy and research.
You could work as a biomedical data scientist, building predictive models to forecast patient outcomes. Maybe you’ll take on a role as a cyber data scientist, analysing complex datasets to uncover potential digital security weaknesses. Or perhaps you’ll apply your expertise in customer analytics, analysing customer behaviours and characteristics data to design more personalised user experiences and targeted marketing campaigns.
Whatever your area of interest, career paths are available in a wide range of areas including:
- Aerospace and defence
- Automation and robotics
- Banking and finance
- Business operations
- Cybersecurity
- Data science analytics
- Environmental monitoring
- Healthcare and biostatistics
- Insurance
- Predictive analytics
- Public health
- Sports analytics
- Technology and software development.
Industry trends
As more and more business are becoming increasingly reliant on data to drive their decision-making, the demand for statisticians and data scientists is also increasing at a rapid rate. In Australia, the job growth for statisticians and data scientists is projected to grow by nearly 30% over the next five years (Randstad, 2024).
In Australia, jobs in the science, technology, engineering and mathematics sectors have proven to be more resilient than jobs in other sectors amid periods of economic instability. Demonstrating the growing and continued importance of STEM skills to the economy now and into the future (Australian Government, 2020).
Ready to apply?
Your study experience and support
Adelaide University sets you up for success in your studies – and your social life. You’ll have access to work placement and internship opportunities, overseas study tours and exchanges, networking events with guest speakers and more. Our campuses are equipped with purpose-built facilities including lecture theatres, libraries, workshops, laboratories, and spaces that simulate real work environments. These are all supported by the latest technologies and a 24/7 online learning platform with personalised study information and resources.
You’ll have everything you need to live well and thrive during your studies, with health services on campus, gymnasiums, technology zones and modern student lounges. Get involved in campus sport or join our student clubs that will connect you to your passions – and the people who share them.
Adelaide also has a variety of accommodation options to suit your individual requirements and budget, with options ranging from dedicated student accommodation to private rentals. One of the world’s most liveable cities, Adelaide has lots of leafy parks, gardens and social hubs – and some of the highest living standards globally. No matter where you are in Adelaide, you’re only a short distance from beaches, vineyards, museums, art galleries, restaurants, bars and parklands. Visit the accommodation web page to find out more.
Student services
We’re here to support you on your student journey. Adelaide University offers a range of support services and facilities, including:
- Career advice and mentoring services
- Personal counselling
- LGBTQIA+ support
- Academic support
- Fees and finance help
- Security services
- Accommodation services
- Common rooms
- Prayer rooms.
You’ll also have unlimited access to our dedicated student support hub. Visit in-person or online, or contact our friendly team by phone. We can assist you with anything study-related including enrolment, identification cards, timetables, fees and more.
Your campus
You'll be studying at one of our renowned campuses, accessing cutting-edge facilities and contemporary study spaces.
Study hours
Your courses will require a combination of different learning formats, including lectures, tutorials, workshops, seminars and practicals. Aside from your classes, you’ll also need to allocate additional time for independent study. This may include assignments, readings, projects and contributing to online discussion forums. As a rough guide, full-time studies may require 12-26 hours of class time and 14-18 hours of independent study per week.
Assessment
During your studies at Adelaide University, you’ll complete a mixture of practical, professional and research-based learning. Your assessment types will vary depending on the degree you’re studying, but may include:
- Case studies
- Essays and assignments
- Examinations
- Group projects
- Internships and placements
- Practicals
- Presentations
- Reports and project documentations
- Research projects
- Workplace and classroom contributions.
