Financial Mathematics and Statistics
Study in the discipline of Financial Mathematics and Statistics is offered by the School of Mathematics and Statistics in the Faculty of Science. Units of study in this major are available at standard and advanced level.
About the major
Financial mathematics and statistics is designed to meet the needs of a particularly popular area of employment for our mathematics graduates. Mathematics is the foundation of the financial world. It allows investors, traders and bankers to make optimal decisions and to distribute risk in a rational way. The mathematics behind finance is, however, not simple and relies heavily on ideas from mathematical theory of probability, analysis, differential equations and statistics.
Financial Mathematics and Statistics will give you a broad introduction to the methods and ideas of mathematical finance and will prepare you for employment in the financial sector or for honours and further study in the field.
Requirements for completion
A major in Financial Mathematics and Statistics requires 48 credit points, consisting of:
(i) 12 credit points of 1000 level units according to the following rules
(a) 3 credit points of calculus units; 3 credit points of multivariable calculus; 3 credit points of linear algebra units and 3 credit points of statistics units*. (Students in the Mathematical Sciences program must choose this option^);or
(b) 3 credit points of calculus units; 3 credit points of linear algebra units and 6 credit points of data science units*
(ii) 18 credit points of 2000-level core units
(iii) 12 credit points of 3000-level core units
(iv) 6 credit points of 3000-level interdisciplinary project units
*Students not enrolled in the BSc may substitute ECMT1010 Introduction to Economic Statistics or BUSS1020 Quantitative Business Analysis
^If elective space allows, students may substitute DATA1001/1901 Foundations of Data Science for the statistics unit
A minor in Financial Mathematics and Statistics requires 36 credit points, consisting of:
(i) 12 credit points of 1000 level units according to the following rules:
(a) 3 credit points of 1000-level calculus units; 3 credit points of multivariable calculus units; 3 credit points of linear algebra units and 3 credit points of statistics units; or
(b) 6 credit points of data science units; 3 credit points of calculus units and 3 credit points of linear algebra units
(ii) 18 credit points of 2000-level core units
(iii) 6 credit points of 3000-level core units
First year
MATH1021/1921/1931 Calculus Of One Variable, MATH1023/1923/1933 Multivariable Calculus and Modelling and MATH1002/1902 Linear Algebra, and either MATH1005/1905 Statistical Thinking with Data or DATA1001/1901 Foundations of Data Science.
The first year units provide a strong foundation for further learning and a broad introduction to the Mathematical Sciences. MATH1021/1921/1931 Calculus of One Variable and MATH1023/1923/1933 Multivariable Calculus and Modelling extend your knowledge of calculus and introduce you to calculus of several variables and mathematical modelling with differential equations. MATH1002/1902 Linear Algebra introduces you to linear algebra, including matrices and their applications. MATH1005/1905 Statistical Thinking with Data and DATA1001/1901 Foundations of Data Science both introduce you to working with data.
Second year
Core: MATH2070/2970 Optimisation and Financial Mathematics, STAT2011/2911 Probability and Estimation Theory, DATA2X02 Data Analytics: Learning from Data.
The second year units provide core specialist knowledge and skills in Financial Mathematics and Statistics. STAT2011/2911 Probability and Estimation Theory provides foundational knowledge of random processes and probability while MATH2070/2970 Optimisation and Financial Mathematics introduces you to mathematical optimisation and the foundations of financial mathematics.
Third year
In your third year you must take the designated project unit.
You must also take MATH3075/3975 Financial Derivatives which introduces you to the world of financial derivatives and STAT3021/3921 Stochastic Processes gives you the tools to understand highly variable financial markets.
Fourth year
The fourth year is only offered within the Bachelor of Advanced Studies course.
Advanced coursework
The Bachelor of Advanced Studies advanced coursework option consists of 48 credit points, with a minimum of 24 credit points at 4000-level or above. Of these 24 credit points, you must complete a project unit of study worth at least 12 credit points.
Honours
Meritorious students may apply for admission to Honours within a subject area of the Bachelor of Advanced Studies. Admission to Honours requires the prior completion of all requirements of the Bachelor's degree, including Open Learning Environment (OLE) units. If you are considering applying for admission to Honours, ensure your degree planning takes into account the completion of a second major and all OLE requirements prior to Honours commencement.
Unit of study requirements for Honours in the area of Financial Mathematics and Statistics: completion of 24 credit points of project work and 24 credit points of coursework.
Contact and further information
W sydney.edu.au/science/schools/school-of-mathematics-and-statistics
First year enquiries email:
Other undergraduate enquiries email:
All enquiries phone: +61 2 9351 5787
School of Mathematics and Statistics
Level 5, Carslaw Building F07
University of Sydney NSW 2006
Professor Mary Myerscough
T 9351 3724
E
Learning Outcomes
Students who graduate from Financial Mathematics and Statistics will be able to:
- Exhibit a broad and coherent body of knowledge in fundamental areas in mathematics and statistics, with a particular focus on optimisation, risk analysis and stochastic processes.
- Interpret information communicated in mathematical or statistical form.
- Identify and address gaps in knowledge and skills by independently sourcing, collating and synthesising appropriate resources that extend their understanding of concepts in financial mathematics and statistics.
- Communicate mathematical information, reasoning and conclusions through a range of modes, to diverse audiences, using evidence-based arguments that are robust to critique.
- Construct logical, clearly presented and justified arguments in mathematics and statistics, including incorporating deductive or evidence-based reasoning.
- Formulate and model practical and abstract problems in mathematical and statistical terms using a variety of methods.
- Address practical and abstract problems in mathematics and statistics with a focus on the financial sector, using a range of concepts, techniques and technologies, working responsibly and ethically and with consideration of cross-cultural perspectives, within collaborative and interdisciplinary teams.