Revision of the BCS 2011 structure. The new structure includes major/minor fields of study, specifically a major/minor in Computational Science. This unit will provide the foundation for this field of study.

02/02/2021 Admin: Update on behalf of CE, modify prerequisites.

21/09/2020 Admin: Update to include new assessment and teaching approach fields as per Handbook requirements.

20/9/2019: Admin - updating exam duration to include additional 10 minutes as per University requirement.

10/04/2019 - Admin: updating workload on behalf of the CE - changing the lecture to a workshop as approved by ADLT (via email), effective Semester 2, 2019.

24/10/2018 - Change of name to better reflect content of the unit and in response to feedback, increase time for tutorials and decrease time for labs in response to feedback and as part of the improvement plan for the unit. To be implemented best case before next iteration 2019/SM2.

07/12/2017 - Updating prerequisites to include the new programming unit.

NOV 2017: Add ENG1005 as an alternative maths prerequisite unit

05/01/2017: Changed Exam writing time (from 3 to 2 hours) and In-semester assessment proportion (from 30% to 40%) to comply with the University policy. This change will be in effect from S2/2017.

Sept 2015: Amendments for course architecture. Effective 2016.

June 2015 - Update to learning outcomes for AQF compliance.

January 2013: Emendation to the assessment breakdown because of collusion in in-semester assessment. Effective semester 2 2013.

September 2012: Added FIT1040 as an alternative prerequisite to FIT1002 for 2013. Approved by CE (Arun Konagurthu) via email 30/08/2012.

Nov 2011 - add MTH1035 to list of approved Maths unit prerequisites on request from Science faculty. MTH1035 is an advanced version of MTH1030. Effective S1, 2012.

While not tied to any particular field of scientific study, this unit requires a general scientific background at advanced introductory level. This unit will cover the role of computational tools and methods in 21st century science, emphasising modelling and simulation. Applications examples will be drawn from Physics, Biology, Bioinformatics, Chemistry, Social Science, etc.

The unit is an elective in the FIT degrees of BCS, BCS-Advanced (Honours) and BInfoTech. It forms a component of the BCS, BCS-Advanced (Honours) and Science Minor/Major sequences in Computational Science.

The unit is compulsory as part of the Computational Science major/minor within the BCS, the Computer Science major/minor offered in the BSc and it is a core unit for the BSc/BCS double degree. It is a Level 3 elective for all other students. The unit provides an introduction to Computational Science for students with a minimal background in Computer Science and at least one other science field of study. No other unit for this purpose exists.

At the completion of this unit, students should be able to:

1. explain and apply the process of computational scientific model building, verification and interpretation;
2. 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 different modelling approaches;
4. rationalise the role of simulation and modelling in science;
5. apply all of the above to solving idealisations of real-world problems across various scientific disciplines.

020199

This unit provides an overview of computational science and an introduction to its central methods. It covers the role of computational tools and methods in 21st century science, emphasising modelling and simulation. It introduces a variety of models, providing contrasting studies on: continuous versus discrete models; analytical versus numerical models; deterministic versus stochastic models; and static versus dynamic models. Other topics include: Monte-Carlo methods; epistemology of simulations; visualisation; high-dimensional data analysis; optimisation; limitations of numerical methods; high-performance computing and data-intensive research.

A general overview is provided for each main topic, followed by a detailed technical exploration of one or a few methods selected from the area. These are applied in tutorials and laboratories which also acquaint students with standard scientific computing software (e.g., Mathematica, Matlab, Numpy, Julia). Applications are drawn from disciplines including Physics, Biology and Social Science.

Scientific Computing - an introductory survey. Michael T. Heath Mc Graw Hill 2002

on campus

Selected lectures on applications and case studies will be given by specialist guest lecturers.

The teaching and learning approach provides facilitated learning and practical exploration of case studies to develop real-world skills.

Examination (2 hours and 10 minutes): 60%; In-semester assessment: 40%

1. Assignment Part 1: - 15% - ULO: 1, 2, 3
2. Assignment Part 2: - 15% - ULO: 1, 2, 3, 4, 5
3. Lab interviews: - 10% - ULO: 1, 2, 3, 4, 5
4. Exam: - 60% - ULO: 1, 2, 3, 4, 5

Minimum total expected workload equals 12 hours per week comprising:

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

(b.) Additional requirements (all students):

Laboratories must follow tutorials

FIT

One of MAT1841, ENG1005, MTH1030, MTH1035 or equivalent, plus any introductory programming unit (e.g. FIT1045, FIT1053, FIT1048, FIT2085, FIT1051, ECE2071, ENG1003, or equivalent)