In 2023, this course is on campus with remote adjustments only for participants with unavoidable travel restrictions/visa delays.
This course teaches introductory programming, fundamental programming language and computer science concepts, and computational problem solving illustrated with applications common in science and engineering, such as simulation and data analysis, visualisation and machine learning models. The course does not require any prior knowledge of programming, computer science or IT. There is an emphasis on designing and writing correct programs: testing and debugging are seen as integral to the programming enterprise.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Design, write and debug programs in the python language to solve practical problems of a scientific or engineering nature.
- Use key modules/libraries for computational analysis and visualisation of scientific and engineering data.
- Demonstrate awareness of good program organisation especially for scientific pipelines.
- Demonstrate understanding of widely-used algorithms and data structures, and their computational complexity.
- Demonstrate understanding of more advanced algorithm design paradigms such as dynamic programming with scientific applications.
- Demonstrate advanced understanding of data types and libraries used for data analysis and machine learning in python, including array-based programming.
Other Information
http://cs.anu.edu.au/courses/COMP1730/
Indicative Assessment
- Practical programming assessments (50) [LO 1,2]
- Written and programming exams (50) [LO 1,2,3,4,5,6]
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.
Workload
4 hours scheduled time each week (2 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.
Inherent Requirements
Not applicable
Requisite and Incompatibility
Prescribed Texts
There are no prescribed texts.
We recommend:
- "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).
Fees
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:
- 2
- 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.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
First Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 4220 | 19 Feb 2024 | 26 Feb 2024 | 31 Mar 2024 | 24 May 2024 | In Person | N/A |
