C04418v2 Master of Data Science in Quantitative Finance
Award(s): Master of Data Science in Quantitative Finance (MDataScQF)CRICOS code: 107831E
Commonwealth supported place?: No
Load credit points: 96
Course EFTSL: 2
Location: City campus
Overview
Career options
Course intended learning outcomes
Admission requirements
Recognition of prior learning
Course duration and attendance
Course structure
Course completion requirements
Course program
Articulation with UTS courses
Other information
Overview
Major regulatory changes and the emergence of new types of financial risks mean that skilled quantitative finance professionals are more in demand than ever. The UTS postgraduate Quantitative Finance program is recognised in Australia and overseas as a leading qualification for aspiring and established quantitative finance professionals.
Designed by industry experts and leading UTS academics, the Master of Data Science in Quantitative Finance combines subjects from the internationally acclaimed UTS Master of Quantitative Finance with specialist study in data science and statistical modelling. Students learn to tackle data problems on new scales and at increasing levels of complexity and emerge ready to deliver advanced analysis and modelling in portfolio optimisation, market and credit risk modelling.
Explore our quantitative finance degrees
UTS offers a suite of postgraduate quantitative finance degrees, each with a different area of focus. Students considering the Master of Mathematics and Quantitative Finance may also be interested in the following courses:
Course content is comprised of 11 subjects, including six subjects from the Master of Quantitative Finance. Students engage with the in-depth study of financial market instruments, probability theory, and credit and market risk, among others, and diversify their skill sets with specialist study in machine learning, Bayesian methods and mathematical research.
They also learn to apply their theoretical learning to industry-relevant assignments in areas such as derivative security pricing and hedging, valuation of financial instruments, market and credit risk measurement and management, and machine learning.
Career options
Graduates are highly sought after by leading financial institutions, management consulting companies, energy and mining companies, regulatory bodies, government organisations and other organisations seeking advanced data science and quantitative finance expertise.
They can work as quantitative analysts, data scientists, data analysts, quantitative structurers, quantitative developers, forecasters, traders, financial engineers, market risk analysts, credit risk analysts, data engineers, data modellers and investment analysts.
Course intended learning outcomes
| 1.1 | Appraise advanced knowledge and critically evaluate and question the information�s source and relevance, with a focus on applications of mathematical methodologies to quantitative finance problem solving. |
| 2.1 | Investigate complex and challenging real-world problems in the areas of quantitative finance by critically evaluating information and solutions and conducting appropriate approaches to independent research. |
| 3.1 | Engage in work practices that demonstrate an understanding of confidentiality requirements, ethical conducts, data management, and organisation and collaborative skills in the context of applying mathematical and statistical modelling to quantitative finance problems. |
| 4.1 | Reflect and evaluate the value, integrity, and relevance of multiple sources of information to derive responsive, innovative solutions, show creativity, innovation and application of technologies in complex quantitative finance problems. |
| 5.1 | Develop 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
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
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
Students may be granted a maximum of 36 credit points of recognition of prior learning.
Course duration and attendance
The course is normally completed in two years of full-time study or four years of part-time study.
Course structure
The course comprises 96 credit points of core subjects.
Course completion requirements
| STM91462 Core Subjects (M Quantitative Finance and Data Science) | 96cp | |
| Total | 96cp |
Course program
Typical full-time programs are provided below, showing a suggested study sequence for students undertaking the course with Autumn and Spring session commencements.
| Autumn commencing, full time | ||
| Year 1 | ||
| Autumn session | ||
| 37005 Fundamentals of Derivative Security Pricing | 8cp | |
| 37011 Financial Market Instruments | 8cp | |
| 37010 Statistics and Financial Econometrics | 8cp | |
| Spring session | ||
| 37004 Interest Rates and Credit Risk Models | 8cp | |
| 37007 Probability Theory and Stochastic Analysis | 8cp | |
| 37009 Risk Management | 8cp | |
| Year 2 | ||
| Autumn session | ||
| 35112 Mathematical Research Project A | 12cp | |
| 37401 Machine Learning: Mathematical Theory and Applications | 8cp | |
| Spring session | ||
| 37400 Postgraduate Optimisation | 8cp | |
| 37457 Advanced Bayesian Methods | 8cp | |
| 35113 Mathematical Research Project B | 12cp | |
| Spring commencing, full time | ||
| Year 1 | ||
| Spring session | ||
| 37004 Interest Rates and Credit Risk Models | 8cp | |
| 37007 Probability Theory and Stochastic Analysis | 8cp | |
| 37009 Risk Management | 8cp | |
| Year 2 | ||
| Autumn session | ||
| 37005 Fundamentals of Derivative Security Pricing | 8cp | |
| 37011 Financial Market Instruments | 8cp | |
| 37010 Statistics and Financial Econometrics | 8cp | |
| Spring session | ||
| 37400 Postgraduate Optimisation | 8cp | |
| 37457 Advanced Bayesian Methods | 8cp | |
| 35113 Mathematical Research Project B | 12cp | |
| Year 3 | ||
| Autumn session | ||
| 35112 Mathematical Research Project A | 12cp | |
| 37401 Machine Learning: Mathematical Theory and Applications | 8cp | |
Articulation with UTS courses
Students who complete C11307 Graduate Certificate in Data Science in Quantitative Finance or C04373 Master of 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