C11307v1 Graduate Certificate of Data Science in Quantitative Finance
Award(s): Graduate Certificate of 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 of 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 of 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: Access and critically analyse large data sets and apply data science techniques to facilitate decision making. |
| 1.2 | Synthesise: Develop a professional identity with breadth and depth of knowledge in data science. |
| 1.3 | Evaluate: Integrate cutting edge concepts in data science to complement your current specialisation and further your career. |
| 2.1 | Analyse: Critically analyse new data science tools to solve to complex real world problems |
| 2.2 | Synthesise: Investigate real-world problems by analysing and critically evaluating different solutions to complex challenges. |
| 2.3 | Evaluate: Evaluate the application of new research in data science to complex real world problems. |
| 3.1 | Analyse: Demonstrate an awareness of ethical responsibilities of a professional working in data science. |
| 3.2 | Synthesise: Develop an awareness of ethical solutions to problems that can result in a major impact on society. |
| 3.3 | Evaluate: Collaborate to implement mathematical solutions to complex real world problems. |
| 4.1 | Analyse: Derive innovative solutions to complex problems using data science techniques. |
| 4.2 | Synthesise: Master data science technical skills necessary for professional practice. |
| 4.3 | Evaluate: Develop the capacity to anticipate and respond to innovation in data science. |
| 5.1 | Analyse: Convey mathematical and statistical results clearly in high quality written form appropriate for their audience. |
| 5.2 | Synthesise: Communicate mathematical solutions. |
| 5.3 | Evaluate: Prepare and deliver professional presentations to different audiences using different media. |
| 6.1 | Analyse: Demonstrate an appreciation of historical and contemporary Aboriginal and Torres Strait Islander Knowledges relevant to mathematics. |
| 6.2 | Synthesise: Develop cultural awareness for ethical and respectful practices when developing community relations. |
| 6.3 | Evaluate: Integrate Aboriginal and Torres Strait Islander knowledges into professional practice. |
Admission requirements
Applicants must have completed a UTS recognised bachelor's degree, or an equivalent or higher qualification, or submitted other evidence of general and professional qualifications that demonstrates potential to pursue graduate studies.
The English proficiency requirement for international students or local applicants with international qualifications is: Academic IELTS: 6.5 overall with a writing score of 6.0; or TOEFL: paper based: 550-583 overall with TWE of 4.5, internet based: 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