Major

# Mathematics and Statistics

### Navigation

## What will I study?

### Overview

You’ll complete this major as part of a Bachelor of Science degree.

This major gives you deep knowledge in one of four specialisations: **Pure Mathematics**, **Applied Mathematics**, **Discrete Mathematics and Operations Research**, and **Statistics and Stochastic Processes**.

In your **first** and **second years** you will complete subjects that are prerequisites for your major, including foundational mathematics and statistics subjects.

In your **third year**, you will complete 50 points (four subjects) of deep and specialised study in your chosen specialisation of mathematics and statistics.

Throughout your degree you will also take science **elective** subjects and **breadth** (non-science) subjects, in addition to your major subjects and prerequisites.

Read more about studying mathematics and statistics at the University of Melbourne.

### Sample course plan

View some sample course plans to help you select subjects that will meet the requirements for this major.

If you did not achieve a study score of at least 29 in VCE Specialist Mathematics 3/4, you may need to enrol in MAST10005 Calculus 1 in your first semester. If you achieved a study score of at least 36 in VCE Specialist Mathematics 3/4 or equivalent, you can enrol in MAST10021 Calculus 2: Advanced and MAST10022 Linear Algebra: Advanced instead of MAST10006 Calculus 2 and MAST10007 Linear Algebra.

Year 1

100 pts

Year 2

100 pts

- Semester 1 50 pts
science elective

12.5 pts

science elective

12.5 pts

breadth

12.5 pts

- Semester 2 50 pts

Year 3

100 pts

- Semester 1 50 pts

- Semester 2 50 pts

If you did not achieve a study score of at least 29 in VCE Specialist Mathematics 3/4, you may need to enrol in MAST10005 Calculus 1 in your first semester. If you achieved a study score of at least 36 in VCE Specialist Mathematics 3/4 or equivalent, you can enrol in MAST10021 Calculus 2: Advanced and MAST10022 Linear Algebra: Advanced instead of MAST10006 Calculus 2 and MAST10007 Linear Algebra.

Year 1

100 pts

- Semester 1 50 pts

- Semester 2 50 pts
science elective

12.5 pts

science elective

12.5 pts

breadth

12.5 pts

Year 2

100 pts

- Semester 1 50 pts

- Semester 2 50 pts

Year 3

100 pts

- Semester 1 50 pts

- Semester 2 50 pts

If you did not achieve a study score of at least 29 in VCE Specialist Mathematics 3/4, you may need to enrol in MAST10005 Calculus 1 in your first semester. If you achieved a study score of at least 36 in VCE Specialist Mathematics 3/4 or equivalent, you can enrol in MAST10021 Calculus 2: Advanced and MAST10022 Linear Algebra: Advanced instead of MAST10006 Calculus 2 and MAST10007 Linear Algebra.

Year 1

100 pts

Year 2

100 pts

- Semester 1 50 pts

- Semester 2 50 pts
science elective

12.5 pts

science elective

12.5 pts

breadth/elective

12.5 pts

Year 3

100 pts

- Semester 1 50 pts

- Semester 2 50 pts

Year 1

100 pts

Year 2

100 pts

- Semester 1 50 pts

- Semester 2 50 pts
science elective

12.5 pts

science elective

12.5 pts

breadth

12.5 pts

Year 3

100 pts

- Semester 1 50 pts

- Semester 2 50 pts

You must have achieved a study score of at least 38 in VCE Specialist Mathematics 3/4 or equivalent to enrol in MAST10008: Accelerated Mathematics 1 AND MAST10009: Accelerated Mathematics 2.

Year 1

100 pts

- Semester 1 50 pts
science elective

12.5 pts

science elective

12.5 pts

breadth

12.5 pts

- Semester 2 50 pts
science elective

12.5 pts

science elective

12.5 pts

breadth

12.5 pts

Year 2

100 pts

- Semester 1 50 pts
science elective

12.5 pts

science elective

12.5 pts

science elective

12.5 pts

breadth

12.5 pts

- Semester 2 50 pts

Year 3

100 pts

- Semester 1 50 pts

- Semester 2 50 pts

Year 1

100 pts

- Semester 2 50 pts
science elective

12.5 pts

science elective

12.5 pts

breadth

12.5 pts

- Summer 12.5 pts

- Semester 1 37.5 pts

Year 2

100 pts

- Semester 2 50 pts

- Semester 1 50 pts
science elective

12.5 pts

science elective

12.5 pts

science elective

12.5 pts

breadth

12.5 pts

Year 3

100 pts

- Semester 2 50 pts

- Semester 1 50 pts

Year 1

100 pts

- Semester 2 50 pts

- Semester 1 50 pts
science elective

12.5 pts

science elective

12.5 pts

breadth

12.5 pts

Year 2

100 pts

- Semester 2 50 pts
science elective

12.5 pts

science elective

12.5 pts

breadth

12.5 pts

- Semester 1 50 pts

Year 3

100 pts

- Semester 2 50 pts

- Semester 1 50 pts

Year 1

100 pts

Year 2

100 pts

- Semester 2 50 pts

- Semester 1 50 pts
science elective

12.5 pts

science elective

12.5 pts

breadth

12.5 pts

Year 3

100 pts

- Semester 2 50 pts

- Semester 1 50 pts

Year 1

100 pts

- Semester 2 50 pts
science elective

12.5 pts

science elective

12.5 pts

breadth

12.5 pts

- Summer 12.5 pts

- Semester 1 37.5 pts

Year 2

100 pts

- Semester 2 50 pts

- Semester 1 50 pts
science elective

12.5 pts

science elective

12.5 pts

breadth

12.5 pts

### Explore this major

Explore the subjects you could choose as part of this major.

- 12.5 pts
Complex analysis is a core subject in pure and applied mathematics, as well as the physical and engineering sciences. While it is true that physical phenomena are given in terms of real numbers and real variables, it is often too difficult and sometimes not possible, to solve the algebraic and differential equations used to model these phenomena without introducing complex numbers and complex variables and applying the powerful techniques of complex analysis.

Topics include:the topology of the complex plane; convergence of complex sequences and series; holomorphic functions, the Cauchy-Riemann equations, harmonic functions and applications; contour integrals and the Cauchy Integral Theorem; singularities, Laurent series, the Residue Theorem, evaluation of integrals using contour integration, conformal mapping; and aspects of the gamma function.

- 12.5 pts
This subject provides a basis for further studies in modern analysis, geometry, topology, differential equations and quantum mechanics.It introduces the idea of a metric space with a general distance function, and the resulting concepts of convergence, continuity, completeness, compactness and connectedness. The subject also introduces Hilbert spaces: infinite dimensional vector spaces (typically function spaces) equipped with an inner product that allows geometric ideas to be used to study these spaces and linear maps between them.

Topics include: metric and normed spaces, limits of sequences, open and closed sets, continuity, topological properties, compactness, connectedness; Cauchy sequences, completeness, contraction mapping theorem; Hilbert spaces, orthonormal systems, bounded linear operators and functionals, applications.

- 12.5 pts
Algebra has a long history of important applications throughout mathematics, science and engineering, and is also studied for its intrinsic beauty. In this subject we study the algebraic laws satisfied by familiar objects such as integers, polynomials and matrices. This abstraction simplifies and unifies our understanding of these structures and enables us to apply our results to interesting new cases. Students will gain further experience with abstract algebraic concepts and methods. General structural results are proved and algorithms developed to determine the invariants they describe.

- 12.5 pts
Graphs model networks of all types such as telecommunication, transport, computer and social networks. They also model physical structures such as crystals and abstract structures within computer algorithms.

This subject is an introduction to the modern field of graph theory. It emphasises the relationship between proving theorems in mathematics and the construction of algorithms to find the solutions of mathematical problems within the context of graph theory. The subject provides material that supplements other areas of study such as operations research, computer science and discrete mathematics

- 12.5 pts
This subject is concerned with the study of objects, which are finite in number and typically computable. At a computational level one seeks efficient algorithms and methods for construction and counting of the objects.

The main topics to be covered are: enumeration, permutations, designs, finite geometry, words, Ramsey theory and physical combinatorics. Designs are relevant to statistics, Ramsey theory to computer science, and physical combinatorics to mathematical physics. Words are useful for representing and constructing objects and relating combinatorial objects to algebraic structures.

- 12.5 pts
This subject introduces three areas of geometry that play a key role in many branches of mathematics and physics. In differential geometry, calculus and the concept of curvature will be used to study the shape of curves and surfaces. In topology, geometric properties that are unchanged by continuous deformations will be studied to find a topological classification of surfaces. In algebraic geometry, curves defined by polynomial equations will be explored. Remarkable connections between these areas will be discussed.

Topics include: Topological classification of surfaces, Euler characteristic, orientability.Introduction to the differential geometry of surfaces in Euclidean space:smooth surfaces, tangent planes, length of curves, Riemannian metrics, Gaussian curvature, minimal surfaces, Gauss-Bonnet theorem.Complex algebraic curves, including conics and cubics, genus.

- 12.5 pts
Complex analysis is a core subject in pure and applied mathematics, as well as the physical and engineering sciences. While it is true that physical phenomena are given in terms of real numbers and real variables, it is often too difficult and sometimes not possible, to solve the algebraic and differential equations used to model these phenomena without introducing complex numbers and complex variables and applying the powerful techniques of complex analysis.

Topics include:the topology of the complex plane; convergence of complex sequences and series; holomorphic functions, the Cauchy-Riemann equations, harmonic functions and applications; contour integrals and the Cauchy Integral Theorem; singularities, Laurent series, the Residue Theorem, evaluation of integrals using contour integration, conformal mapping; and aspects of the gamma function.

- 12.5 pts
Most mathematical problems arising from the physical sciences, engineering, life sciences and finance are sufficiently complicated to require computational methods for their solution. This subject introduces students to the process of numerical approximation and computer simulation, applied to simple and commonly encountered stochastic or deterministic models. An emphasis is on the development and implementation of algorithms for the solution of continuous problems including aspects of their efficiency, accuracy and stability. Topics covered will include simple stochastic simulation, direct methods for linear systems, data fitting of linear and nonlinear models, and time-stepping methods for initial value problems.

- 12.5 pts
Stochastic processes occur in finance as models for asset prices, in telecommunications as models for data traffic, in computational biology as hidden Markov models for gene structure, in chemistry as models for reactions, in manufacturing as models for assembly and inventory processes, in biology as models for the growth and dispersion of plant and animal populations, in speech pathology and speech recognition and many other areas.

This course introduces the theory of stochastic processes including Poisson processes, Markov chains in discrete and continuous time, and renewal processes. These processes are illustrated using examples from real-life situations. It then considers in more detail important applications in areas such as queues and networks (the foundation of telecommunication models), finance, and genetics.

- 12.5 pts
This subject demonstrates how the mathematical modelling process naturally gives rise to certain classes of ordinary and partial differential equations in many contexts, including the infectious diseases, the flow of traffic and the dynamics of particles and of fluids. It advances the student’s knowledge of the modelling process, as well addressing important mathematical ideas in deterministic modelling and the challenges raised by system nonlinearity.

- Infectious disease models and other contexts leading to systems of autonomous first-order differential equations; initial value problem, phase space, critical points, local linearization and stability; qualitative behaviour of plane autonomous systems, structural stability; formulation, interpretation and critique of models.
- Conservation laws and flux functions leading to first-order quasilinear-linear partial differential equations; characteristics, fans, shocks and applications including modelling traffic flow.
- Introduction to continuum mechanics: basic principles; tensor algebra and tensor calculus; the ideal fluid model and potential flow; the Newtonian fluid, Navier-Stokes equations and simple solutions.

- 12.5 pts
This subject builds on, and extends earlier, related undergraduate subjects with topics that are useful to applied mathematics, mathematical physics and physics students, as well as pure mathematics students interested in applied mathematics and mathematical physics. These topics include:

- Special functions: Spherical harmonics including Legendre polynomials and Bessel functions, including cylindrical, modified and spherical Bessel functions;
- Integral equations: Classification, Fourier and Laplace transform solutions, separable kernels, singular integral equations, Wiener-Hopf equations, and series solutions;
- Further vector analysis: Differential forms, and integrating p-forms;
- Further complex analysis: The Schwarz reflection principle, and Wiener-Hopf in complex variables.

- 12.5 pts
This subject is concerned with the study of objects, which are finite in number and typically computable. At a computational level one seeks efficient algorithms and methods for construction and counting of the objects.

The main topics to be covered are: enumeration, permutations, designs, finite geometry, words, Ramsey theory and physical combinatorics. Designs are relevant to statistics, Ramsey theory to computer science, and physical combinatorics to mathematical physics. Words are useful for representing and constructing objects and relating combinatorial objects to algebraic structures.

- 12.5 pts
This subject introduces some major techniques and algorithms for solving nonlinear optimisation problems. Unconstrained and constrained systems will be considered, for both convex and non-convex problems. The methods covered include: interval search techniques, Newton and quasi-Newton methods, penalty methods for nonlinear programs, and methods based on duality. The emphasis is both on being able to apply and implement the techniques discussed, and on understanding the underlying mathematical principles. Examples involve the formulation of operations research models for linear regression, multi-facility location analysis and network flow optimisation.

A significant part of the subject is the project, where students work in groups on a practical operations research problem.

- 12.5 pts
Complex analysis is a core subject in pure and applied mathematics, as well as the physical and engineering sciences. While it is true that physical phenomena are given in terms of real numbers and real variables, it is often too difficult and sometimes not possible, to solve the algebraic and differential equations used to model these phenomena without introducing complex numbers and complex variables and applying the powerful techniques of complex analysis.

Topics include:the topology of the complex plane; convergence of complex sequences and series; holomorphic functions, the Cauchy-Riemann equations, harmonic functions and applications; contour integrals and the Cauchy Integral Theorem; singularities, Laurent series, the Residue Theorem, evaluation of integrals using contour integration, conformal mapping; and aspects of the gamma function.

- 12.5 pts
Stochastic processes occur in finance as models for asset prices, in telecommunications as models for data traffic, in computational biology as hidden Markov models for gene structure, in chemistry as models for reactions, in manufacturing as models for assembly and inventory processes, in biology as models for the growth and dispersion of plant and animal populations, in speech pathology and speech recognition and many other areas.

This course introduces the theory of stochastic processes including Poisson processes, Markov chains in discrete and continuous time, and renewal processes. These processes are illustrated using examples from real-life situations. It then considers in more detail important applications in areas such as queues and networks (the foundation of telecommunication models), finance, and genetics.

- 12.5 pts
Graphs model networks of all types such as telecommunication, transport, computer and social networks. They also model physical structures such as crystals and abstract structures within computer algorithms.

This subject is an introduction to the modern field of graph theory. It emphasises the relationship between proving theorems in mathematics and the construction of algorithms to find the solutions of mathematical problems within the context of graph theory. The subject provides material that supplements other areas of study such as operations research, computer science and discrete mathematics

- 12.5 pts
This subject introduces the essential features of decision-making techniques encountered in operations research, management, industry, business and economics. It shows how to construct formal mathematical models for practical decision-making as encountered in two-person games, multi-objective optimisation problems, stochastic decision problems, group decision and social choice, and decision-making under uncertainty. It shows students further uses of linear programming and introduces dynamic programming techniques.

- 12.5 pts
Linear models are central to the theory and practice of modern statistics. They are used to model a response as a linear combination of explanatory variables and are the most widely used statistical models in practice. Starting with examples from a range of application areas this subject develops an elegant unified theory that includes the estimation of model parameters, quadratic forms, hypothesis testing using analysis of variance, model selection, diagnostics on model assumptions, and prediction. Both full rank models and models that are not of full rank are considered. The theory is illustrated using common models and experimental designs.

- 12.5 pts
Linear models are central to the theory and practice of modern statistics. They are used to model a response as a linear combination of explanatory variables and are the most widely used statistical models in practice. Starting with examples from a range of application areas this subject develops an elegant unified theory that includes the estimation of model parameters, quadratic forms, hypothesis testing using analysis of variance, model selection, diagnostics on model assumptions, and prediction. Both full rank models and models that are not of full rank are considered. The theory is illustrated using common models and experimental designs.

- 12.5 pts
Stochastic processes occur in finance as models for asset prices, in telecommunications as models for data traffic, in computational biology as hidden Markov models for gene structure, in chemistry as models for reactions, in manufacturing as models for assembly and inventory processes, in biology as models for the growth and dispersion of plant and animal populations, in speech pathology and speech recognition and many other areas.

This course introduces the theory of stochastic processes including Poisson processes, Markov chains in discrete and continuous time, and renewal processes. These processes are illustrated using examples from real-life situations. It then considers in more detail important applications in areas such as queues and networks (the foundation of telecommunication models), finance, and genetics.

- 12.5 pts
This subject introduces a measured-theoretic approach to probability theory and presents its fundamentals concepts and results.

Topics covered include: probability spaces and random variables, expectation, conditional expectation and distributions, elements of multivariate distribution theory, modes of convergence in probabilty theory, characteristics functions and their application in key limit theorems.

- 12.5 pts
Modern applied statistics combines the power of modern computing and theoretical statistics. This subject considers the computational techniques required for the practical implementation of statistical theory, and includes Bayes and Monte-Carlo methods. The subject focuses on the application of these techniques to generalised linear models, which are commonly used in the analysis of categorical data.