Numerical Methods & Scientific Computing (MAST30028)

Undergraduate level 3Points: 12.5On Campus (Parkville)

You’re viewing the 2025 Handbook:
Or view archived Handbooks

Overview

Availability
Semester 2
FeesLook up fees

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.

Intended learning outcomes

On completion of this subject, students should:

  • Understand the significance and role of both roundoff error and truncation error in some standard problems in scientific computing;
  • Be able to write simple numerical programs that utilize a numerical Problem-Solving Environment such as Matlab or NumPy;
  • Appreciate the role of computer simulation, as a third method in science, distinct from theory and experiment
  • Understand the distinction between the simulation of stochastic and deterministic models
  • Be able to use appropriate numerical techniques when undertaking a mathematical or modelling investigation

Generic skills

In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include:

  • problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
  • analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
  • collaborative skills: the ability to work in a team;
  • time-management skills: the ability to meet regular deadlines while balancing competing commitments;
  • computer skills: the ability to use mathematical computing packages.

Last updated: 4 March 2025

Eligibility and requirements

Prerequisites

One of

Code Name Teaching period Credit Points
MAST10009 Accelerated Mathematics 2
Semester 2 (On Campus - Parkville)
 12.5
MAST20026 Real Analysis
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
 12.5
MAST20033 Real Analysis: Advanced
Semester 1 (On Campus - Parkville)
 12.5

AND

One of

Code Name Teaching period Credit Points
MAST10007 Linear Algebra
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
Summer Term (On Campus - Parkville)
 12.5
MAST10008 Accelerated Mathematics 1
Semester 1 (On Campus - Parkville)
 12.5
MAST10022 Linear Algebra: Advanced
Semester 1 (On Campus - Parkville)
 12.5

AND

Either:

One of

Code Name Teaching period Credit Points
COMP10001 Foundations of Computing
Semester 1 (On Campus - Parkville)
Summer Term (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5
COMP20005 Intro. to Numerical Computation in C
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
 12.5
PHYC20013 Laboratory and Computational Physics 2
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
 12.5

OR

Other evidence of competence in computer programming.

AND

Other evidence could include passing a relevant Year 12 school subject, or a statement of achievement from a relevant MOOC, or passing a programming competency test administered by another University of Melbourne School.

Corequisites

None

Non-allowed subjects

None

Inherent requirements (core participation requirements)

The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.

Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home

Last updated: 4 March 2025

Assessment

DescriptionTimingPercentage

Two computational assignments, one due mid-semester and one late in semester, requiring approximately 44 hours of work.(*)

  • 44 hours (of work required)
During the teaching period40%

One computer laboratory examination

  • 3 hours
During the examination period60%

Last updated: 4 March 2025

Dates & times

  • Semester 2
    CoordinatorAlex Browning
    Mode of deliveryOn Campus (Parkville)
    Contact hours2 x one hour lectures and 1 x two hour computer laboratory class per week
    Total time commitment170 hours
    Teaching period28 July 2025 to 26 October 2025
    Last self-enrol date 8 August 2025
    Census date 1 September 2025
    Last date to withdraw without fail26 September 2025
    Assessment period ends21 November 2025

    Semester 2 contact information

    Alex.Browning@unimelb.edu.au

Time commitment details

Estimated total time commitment of 170 hours

What do these dates mean

Visit this webpage to find out about these key dates, including how they impact on:

  • Your tuition fees, academic transcript and statements.
  • And for Commonwealth Supported students, your:
       -  Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
     

    Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.

Last updated: 4 March 2025

Further information

Last updated: 4 March 2025