C11307v1 Graduate Certificate in Data Science in Quantitative Finance
Award(s): Graduate Certificate in Data Science in Quantitative Finance (GradCertDataScQF)CRICOS code: 104626A
Commonwealth supported place?: No
Load credit points: 24
Course EFTSL: 0.5
Location: City campus
Overview
Career options
Course intended learning outcomes
Admission requirements
Recognition of prior learning
Course structure
Course completion requirements
Articulation with UTS courses
Transfer between UTS courses
Other information
Overview
The Graduate Certificate in Data Science in Quantitative Finance is designed to meet the evolving “modelling needs” of the quantitative finance industry in data science and analysis. The program, comprising of machine learning theory and applications and advanced quantitative methods, provides participants with additional specialised skill sets – specifically in data science – that are in high demand, even among the more traditional financial organisations.
This data science program also enables Master of Quantitative Finance graduates to extend the specialist skills and expertise of the quantitative finance specialisation through completion of the Graduate Certificate in Data Science in Quantitative Finance as a standalone graduate certificate.
The subjects in this course are technical in nature and focus on quantitative finance applications through examples and case studies. The course aims to extend technical skills acquired in quantitative finance or other technical specialisations.
Career options
Career options include data analyst, data scientist, forecaster, market risk analyst, credit risk analyst, data modeller and data engineer.
Course intended learning outcomes
| 1.1 | Analyse advanced knowledge and critically evaluate the information's source and relevance, with a focus on applications of mathematical methodologies to quantitative finance problem solving. |
| 2.1 | Apply research to complex real-world problems in the areas of quantitative finance by critically evaluating information and solutions and conducting appropriate approaches to independent research. |
| 3.1 | Work ethically and confidentially in an organised and collaborative way whilst managing data and applying mathematical and statistical modelling to quantitative finance problems. |
| 4.1 | Reflect on the value, integrity, and relevance of multiple sources of information to derive creative solutions using technologies to solve quantitative finance problems. |
| 5.1 | Identify and present complex ideas and justifications using appropriate communication approaches from a variety of methods (oral, written, visual) to communicate with mathematicians, data analysts, scientists, industry, and the general public. |
| 6.1 | Critically reflect on Indigenous Australian contexts to inform professional cultural capability to work effectively with and for, Indigenous Australians within Mathematical, Statistical, and Finance contexts. |
Admission requirements
To be eligible for admission to this course, applicants must meet the following criteria.
Applicants must have the following:
- Completed Australian bachelor's degree or higher qualification, or overseas equivalent, in a related discipline with a substantial quantitative component in mathematics, statistics, engineering, computer science, physical sciences, econometrics, or mathematical finance
The English proficiency requirement for international students or local applicants with international qualifications is: IELTS Academic: 6.5 overall with a writing score of 6.0; or TOEFL iBT: 79-93 overall with a writing score of 21; or AE5: Pass; or PTE: 58-64 with a writing score of 50; or C1A/C2P: 176-184 with a writing score of 169.
Eligibility for admission does not guarantee offer of a place.
International students
This course is not offered to International students for direct entry.
Visa requirement: To obtain a student visa to study in Australia, international students must enrol full time and on campus. Australian student visa regulations also require international students studying on student visas to complete the course within the standard full-time duration. Students can extend their courses only in exceptional circumstances.
Recognition of prior learning
No exemptions are granted as recognition of prior learning.
Course structure
Students are required to complete 24 credit points of core subjects.
Course completion requirements
| STM91461 Core Subjects (Grad Cert Data Science) | 24cp | |
| Total | 24cp |
Articulation with UTS courses
Students who complete C11307 Graduate Certificate in Data Science in Quantitative Finance can transfer into C04418 Master of Data Science in Quantitative Finance and receive full recognition of prior learning for the subjects already completed.
Transfer between UTS courses
Students who complete C11307 Graduate Certificate in Data Science in Quantitative Finance can transfer into C04418 Master of Data Science in Quantitative Finance and receive full recognition of prior learning for the subjects already completed.
Other information
Further information is available from:
UTS Student Centre
telephone 1300 ask UTS (1300 275 887)
or +61 2 9514 1222
Ask UTS