Faculty of Information Technology

Monash University

Undergraduate - Unit

This unit entry is for students who completed this unit in 2015 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

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6 points, SCA Band 2, 0.125 EFTSL

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.

FacultyFaculty of Information Technology
OfferedClayton Second semester 2015 (Day)


The unit provides an overview of computational science and an introduction to the central methods in this field. While it is not tied to any particular field of scientific study, it requires a general scientific background at advanced introductory level.

Topics include: the role of computational tools and methods in 21st century science; modelling and simulation; continuous vs discrete models; analytic versus numeric models; deterministic versus stochastic models; Monte-Carlo methods; epistemology of simulations; visualisation; high-dimensional data analysis; optimisation; limitations of numerical methods; high-performance computing and data-intensive research.

Each topic area will be introduced with a general overview followed by a discussion of one or a few selected methods in full technical detail. These will be practiced in tutorials and laboratories, which will also acquaint the students with standard software packages for scientific computing (for example, Mathematica, Matlab, Maple, Sage).

Seminars and guest lectures will present case studies and link to current topics in research.

Applications examples will be drawn from Physics, Biology, Bioinformatics, Chemistry, Social Science, etc.


Upon successful completion of the unit, students will:

  1. apply the process of computational scientific model building, verification and interpretation;
  2. develop and analyse the differences between core classes of modelling approaches (Numerical versus Analytical; Linear versus Non-linear; Continuous versus Discrete; Deterministic versus Stochastic);
  3. evaluate the implications of choosing a particular modelling approach over other possible approaches;
  4. explain the role of simulation and data visualisation in science;
  5. solve idealisations of several real-world problems across various scientific disciplines.


Examination (3 hours): 75%, In-semester assessment: 25%

Workload requirements

Minimum total expected workload equals 12 hours per week comprising:

(a.) Contact hours for on-campus students:

  • Two hours of lectures
  • One 3-hour laboratory
  • One 1-hour tutorial/seminar

(b.) Additional requirements (all students):

  • A minimum of 6 hours independent study per week for completing lab and assignment work, private study and revision.

See also Unit timetable information

Chief examiner(s)

This unit applies to the following area(s) of study


One of MAT1841, MAT2003, ENG1091, MTH1030, MTH1035 or equivalent plus any introductory programming unit (eg FIT1040, FIT1002, ECE2071, TRC2400, or equivalent)

Additional information on this unit is available from the faculty at: