Bachelor of Mathematics (Honours) majoring in Statistics

Undergraduate | 2026

Mode

On Campus

Start date

February, July

Campus

Adelaide City Campus

Duration

4 year(s) full-time

Program code

HMATH

Study as

Full-time

Indicative annual fees

$54,900

Non-tuition fees

Prerequisite

SACE Stage 2 Mathematical Methods (or equivalent)

Assumed knowledge

None

English language requirements

IELTS Overall 6.5 IELTS Reading 6 IELTS Listening 6 IELTS Speaking 6 IELTS Writing 6 Equivalent English qualifications

CRICOS code

115729C

Entry requirements

Admission criteria

To be eligible, an applicant must have achieved at least one of the following minimum entry requirements and demonstrate they fulfil any prerequisite and essential criteria for admission. In cases where there are more eligible applicants than available places, admission will be competitive with ranks based on the entry criteria.

Secondary education (Year 12)

Completion of a secondary education qualification equivalent to the South Australian Certificate of Education (SACE).

Vocational Education and Training (VET)

Completion of an award from a registered training organisation (RTO) at Certificate IV (AQF level 4) or higher.

Higher education study

Successful completion of at least 6 months full-time study (or equivalent part-time) in a higher education award program in an undergraduate diploma (AQF level 5) or higher.

English language entry requirements

In addition, international students who speak English as an additional language must have obtained one of the following standards within the last two years prior to admission. Possession of one or more of these qualifications, in addition to the academic entry requirements, does not, in itself, guarantee a place at Adelaide University. Applications are considered on an individual basis and selection is competitive. Where previous study/work experience was conducted in English, the application must be accompanied by certified documentation from the educational institution/employer certifying that the language of instruction/employment was English.

IELTS Overall 6.5
IELTS Reading 6
IELTS Listening 6
IELTS Speaking 6
IELTS Writing 6

Please access the following link for a comprehensive list of English language tests accepted by Adelaide University and other important information in relation to meeting the University’s language requirements:

Equivalent English qualifications

International admissions by country

Country	Requirement	Score
Australia	ATAR	80
Bangladesh	Higher Secondary Certificate (HSC)	4.90
Canada	Ontario Secondary School Diploma (OSSD)	75%
China	Gaokao	70%
Denmark	Studentereksamen (stx: Upper Secondary School Diploma)	4 (Fair)
France	French Baccalaureate	12.0
Global	International Baccalaureate	28
Hong Kong	Diploma of Secondary Education (HKDSE)	18
India	CBSE and CISCE	80
India	State Board Examinations	90
Indonesia	SMA III	80%
Kenya	Certificate of Secondary Education (KCSE)	B+
Malaysia	Matrikulasi	3.00
Malaysia	Sijil Tinggi Persekolahan Malaysia (STPM)	3.00
Malaysia	United Entrance Certificate (UEC)	19
Nepal	National Examinations Board (NEB)	3.21
Norway	Upper Secondary School Certificate (Vitnemal fra den Videregaende Skole) / Vitnemal For Videregaende Opplaering	4.0
Phillippines	High School Diploma (Grade 12) (Academic Track) K12	88%
Singapore	Singapore GCE Advanced Levels	9
South Korea	College Scholastic Ability Test (CSAT)	330
Sri Lanka	GCE A Levels	10
Sweden	Upper Secondary School Leaving Certificate	15.0
Taiwan	GSAT %	70%
Thailand	Matayom 6	3.60
UK / Global	GCE Advanced Levels	9
USA / Global	Advanced Placement (AP)	9
USA / Global	America College Test (ACT)	24
USA / Global	Scholastic Assessment Test (SAT)	1170
Vietnam	Bằng Tốt Nghiệp Trung Học Phổ Thông (Vietnamese Year 12)	8.3

Why Bachelor of Mathematics (Honours)?

Harness data and drive decision making.

Statisticians are key in helping us make wise, well-informed decisions. Skilled in collecting, organising and making sense of complex data, their expertise is vital to many industries – including healthcare, education, finance, insurance, marketing and more.

Whether they’re assessing clinical data to determine the effectiveness of new medicines, modelling environmental data to guide land or water management policies, or analysing athlete performance to inform coaching strategies – statisticians are crucial in facilitating data-driven decision making.

Develop a skillset that’s transferable, in high-demand and necessary to all industry sectors.

Overview

Discover how to transform raw data into actionable insights 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 Statistics major enables you to gain critical skills in statistical modelling and apply these across a range of disciplines and situations. Apply logical thinking, problem solving and creativity to deliver results. Develop your analytical and communication skills to inform and analyse datasets and explore the use of statistics in areas like public health, environmental science, space and finance. 

You’ll also have multiple opportunities throughout your studies to sharpen 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.

Key features

Build knowledge in statistical methods of collecting, modelling and analysing data.
Gain understanding of the core theories, principles and techniques that underpin the mathematical sciences.
Explore the ethical use and communication of statistical data.
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 Statistics, 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, data science, geometry and more.

In second year, you’ll tackle advanced topics in statistical practice, multivariable calculus, differential equations and algebra. You’ll also complete two elective courses, allowing you to specialise further in areas of interest.

You’ll continue to take core, statistics-driven courses in third and fourth year. 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.

You’ll also complete one elective course from the following:

Advanced differential equations
Advanced mathematical modelling
Advanced optimisation
Complex networks
Cryptography
Data science practice
Fluid dynamics
Functional analysis
Geometry of surfaces
Groups, rings and fields
Introduction to topological data analysis
Mathematical biology
Measure and integration
Metric and topological spaces
Number theory
Simulation for decision science
Text and social media analytics.

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-driven 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 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 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 Statistics major courses, and
6 units from Level 1 Statistics major courses, and
6 units from Level 2 Statistics major courses, and
18 units from Level 3 Statistics major courses

	Course code	Units
Course name Probability	Course code STATX200	Units 6
Course name Statistical Theory	Course code STAT2003	Units 6
Course name Statistical Methodology	Course code STATX302	Units 6

	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 code	Units
Course name Data Visualisation	Course code MATHX314	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 Algebra	Course code MATHX201	Units 6
Course name Real Analysis	Course code MATHX206	Units 6
Course name Optimisation	Course code MATHX205	Units 6
Course name Design of Experiments	Course code STATX500	Units 6

	Course code	Units
Course name Advanced Statistical Theory	Course code STAT3005	Units 6
Course name Computational Statistics	Course code STAT3006	Units 6
Course name Time Series Analysis	Course code MATHX313	Units 6
Course name Stochastic Processes	Course code STATX303	Units 6
Course name Bayesian Statistical Practice	Course code STATX300	Units 6

Complete exactly 18 units from the following:

	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 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 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 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 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 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 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 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 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 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 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 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 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 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-driven roles. Highly sought-after for their advanced technical skills in mathematical modelling and statistical analysis, our mathematics graduates regularly progress into careers in finance and banking, business, technology, healthcare, engineering, government policy and research.

You could work as a clinical statistician, analysing and interpreting large and complex datasets from clinical trials to provide actionable insights to stakeholders. Maybe you’ll take on a role as a quality control statistician, ensuring engineering processes and products meet high quality and safety standards. Or perhaps you’ll apply your expertise as an environmental statistician, analysing environmental data to evaluate the effectiveness of policies and practices.

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 become increasingly reliant on data to drive their decision-making, the demand for statisticians and data scientists who can capture and make sense of this data is also increasing at a rapid rate. In Australia, the job growth for statisticians and data scientists is projected to grow by nearly 30% in 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).

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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
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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.

This degree is offered at the following campus(es)

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.

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Scholarships

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Bachelor of Mathematics (Honours) majoring in Statistics