- Code COMP1730
- Unit Value 6 units
This course has been adjusted for remote participation in Semester 1 2022.
This course teaches introductory programming within a problem solving framework applicable to the sciences. The course emphasises technical programming, the simulation of scientific systems and the processing of scientific data. There is an emphasis on designing and writing correct code. Testing and debugging are seen as integral to the programming enterprise. Both top-down and object oriented design are taught. There will be an introduction to widely-used computer science algorithms and to machine architecture. The course will be taught using one or more programming languages which are widely applicable to scientific work.
Upon successful completion, students will have the knowledge and skills to:
Students who succeed in all aspects of this course will be able to:
- Design, write and debug small programs to solve practical problems of a scientific nature.
- Have a practical understanding of the processing of scientific data.
- Be able to describe and design small computer programs using both procedural and object-oriented methodologies.
- Have an awareness of good program organisation.
- Have an understanding of some widely-used algorithms.
- Have an understanding of practical aspects of machine architecture including finite precision and rounding errors.
Practical programming assessments (55%), written exams (45%).
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
4-5 hours scheduled time each week (2-3 lectures and one 2-hour lab).
Students are expected to spend an average of 5-6 hours per week practicing programming (including work on assignments) outside of scheduled labs.
Requisite and Incompatibility
There are no prescribed texts.
We recommend one of:
- "Think Python: How to think like a computer
scientist" (2nd Edition) by Allan Downey.
Available from http://greenteapress.com/wp/think-python-2e/, or in paperback (O'Reilly, 2015; ISBN-13: 978-1491939369; ISBN-10: 1491939362).
- "The Practice of Computing using Python" (2nd Edition) by William Punch and Richard Enbody (Addison-Wesley, 2012. ISBN-10: 0-13-280557-X ISBN-13: 978-0-13-280557-X).
Students are assumed to have achieved a level of knowledge of mathematics comparable to at least ACT Mathematics Methods or NSW Mathematics or equivalent. No programming, Computer Science or IT experience or skills are required
Areas of Interest
- Medical Science
- Advanced Computing
- Biomedical Science
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
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