This course will teach the basics of programming and computational skills for economic analysis and enable the students to take numerical approach to familiar mathematical problems. Students will learn to graphically represent familiar ideas such as supply and demand curves, equilibrium prices and consumer choice. They will explore how these choices and equilibria change with shifts in policy instruments, preferences and technologies. In the process they will learn to use common computational solution methods, such as root finding and optimization. Students will also learn how to obtain, manipulate and represent data, using tools such as scatterplots and histograms.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Upon a successful completion of this course, students should be able to:- Algorithm and data manipulation and visualization of economic data
- Basic programming skills (conditions, loops, flow control, iteration, etc.)
- Ability to implement familiar mathematical methods on a computer
- Reinforcement of key ideas from economic analysis
- Algorithm and data manipulation and visualization of economic data
Other Information
See the course outline on the College courses page. Outlines are uploaded as they become available.Indicative Assessment
- Individual test-assignment with feedback (by week 4)
- Midterm exam (40%)
- Final exam (60%)
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
2 hour lecture + 2 hour tutorial in computer labRequisite and Incompatibility
Prescribed Texts
Jérôme Adda, Russell W. Cooper “Dynamic Economics: Quantitative Methods and Applications”, MIT Press, 2003Preliminary Reading
Edward R. Tufte. The Visual Display of Quantitative Information. Graphics Press, 2001- R K Sundaram. A First Course in Optimization Theory. Cambridge University Press,1996.
- Kevin Sheppard. Introduction to Python for Econometrics, Statistics and Data AnalysisKevinSheppard.com (August 05, 2014) https://www.kevinsheppard.com/images/0/09/Python_introduction.pdf
- Quantitative Economics online resource
Assumed Knowledge
General knowledge of math and basic economicsFees
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:
- 34
- 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 |
Course fees
- Domestic fee paying students
| Year | Fee | Description |
|---|---|---|
| 2025 | $5280 | Standard Rate |
| 2025 | $3840 |
Grandfathered Rate*
*continuing students in nominated programs only. See fee website |
- International fee paying students
| Year | Fee |
|---|---|
| 2025 | $6720 |
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.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 7787 | 27 Jul 2026 | 03 Aug 2026 | 31 Aug 2026 | 30 Oct 2026 | In Person | N/A |