This course offers students an opportunity to become familiar with and apply the mathematical methods commonly used in contemporary economic analysis. Those methods would include the calculus of several variables, matrix and linear algebra, basic topology, optimisation, and probability theory.

20 Lectures (10 x 2-hour lectures) and 10 tutorials (10 x 1-hour)

2-hour revision lecture

Tutorials are held at various times and can be selected on MyCampus

None.

ILO (covered)

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

The course aims to

- Equip students with a level of mathematical training adequate for contemporary Economics science

- Provide students with computational skills and technical knowledge, necessary to understand and apply Economics concepts at an advanced level;

- Provide students with the skills and knowledge to be successful in the quantitative-intensive job market as well as preparing students for postgraduate study as part of their career path.

By the end of this course students will be able to:

1.  Formulate and explain core mathematical concepts and methods, used in contemporary economics

2.  Describe and Illustrate how various mathematical constructs within the covered material can indicate economic and/or statistical relationships

3.  Use formal-logical reasoning and to summarise it in clearly written arguments

4. Solve relevant economics models by applying mathematical computational skills to obtain results

5. Derive, solve and explain various optimisation problems

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.