Faculty of Information Technology
|Faculty||Faculty of Information Technology|
|Monash Passport category||Depth (Enhance Program)|
|Offered||Not offered in 2014|
This unit can be taken by a maximum of 25 students (due to use of specialised facilities and method of teaching). Selection is on a first-in, first enrolled basis.
Research has experienced profound methodological changes in the last decades. A significant part of scientific enquiry now relies on computational approaches to complement theory and experiment. This a fundamental shift. In the words of Nobel laureate Ken Wilson: computation has become the "third leg" of science. Simulations allow us to perform virtual experiments that are too dangerous, too costly, unethical, or plainly impossible to conduct in reality. Visualisation offers us entirely new ways to explore and understand data, and only computational analysis makes it possible to cope with the vast amounts of data that contemporary science and engineering must process.
Computational science and eResearch are core drivers of innovation. Bioinformatics, climate studies, and ecological modelling are among the most prominent and most important examples, but the fundamental impact of this shift is felt far beyond the so-called "hard" sciences.
Arguably, one of the pivotal influences of computational science is to change the character of whole disciplines by making it possible for them to perform "hard" qualitative data-based studies in areas where this was impossible before. For example, social science researchers can conduct quantitative studies by simulating virtual societies in order to understand the ramifications of hypothetical changes in behaviour or policies. Medical researchers can simulate the spread of world-wide epidemics to evaluate possible containment methods, and economists can use simulations to "measure" the impact of such epidemics and other disasters on national and global financial systems.
This unit will equip students with a thorough understanding of how computational science relates to and extends traditional methods. Students will have the opportunity to work on problems from their "home discipline" which will enable them to understand the potential and limitations of computational studies in these fields.
Topics include: history of science; the role of computational methods; simulations and virtual experiments; capturing complex systems; the limits of modelling; is computational science a paradigm shift?; data-intensive research; virtual collaboration; the scope of e-Research.
On successful completion of this unit, students will have:
An awareness of:
An understanding of:
The ability to:
Examination (3 hours): 60%; In-semester assessment: 40%
Minimum total expected workload equals 12 hours per week comprising:
(a.) Contact hours for on-campus students:
(b.) Additional requirements (all students):