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
The Master of Data Science in Quantitative Finance provides students with cutting-edge skills, knowledge and tools allowing them to tackle data problems on a new scale and complexity for tasks in portfolio optimisation, market modelling and credit risk. Data science techniques including machine learning have already transformed the world of asset management, changing how sophisticated investors approach portfolio construction and risk management.
This course combines an internationally recognised quantitative finance program with skills and knowledge in financial data science and statistical modelling.
Career options
Career opportunities for graduates include quantitative analyst, data scientist, data analyst, quantitative analyst, quantitative structurer, quantitative developer, forecaster, trader, model verification, financial engineer, market risk analyst, credit risk analyst, data engineer, data modeller, and investment analyst and financial engineer across investment banks, trading banks, hedge funds, investment management firms, consulting companies, energy and mining companies, regulatory bodies and government organisations
Course intended learning outcomes
| 1.1 | Analyse: access and critically analyse large financial data sets and apply complex financial models and data science techniques to facilitate decision making in financial trading and risk management contexts. |
| 1.2 | Synthesise: Demonstrate specialised technical expertise in the field of quantitative finance and data science as expected for a senior professional position in industry, commerce or government. |
| 1.3 | Evaluate: Critically analyse, question and evaluate implications of alternative and new models and strategies for financial market trading and risk management. |
| 2.1 | Analyse: Critically analyse new financial models to address financial trading and risk management issues. |
| 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 quantitative finance and data science to complex real world problems. |
| 3.1 | Analyse: Demonstrate an awareness of ethical responsibilities of a professional working in the financial sector. |
| 3.2 | Synthesise: Develop an awareness of ethical solutions to quantitative finance problems that can result in systemic risk and major impact on society. |
| 3.3 | Evaluate: Collaborate to implement mathematical and data science solutions to complex problems arising in the finance and related sectors. |
| 4.1 | Analyse: Derive innovative solutions to complex problems in quantitative finance. |
| 4.2 | Synthesise: Master the theoretical and practical technical skills in quantitative finance and data science necessary for professional practice. |
| 4.3 | Evaluate: Develop the capacity to anticipate and respond to change in quantitative finance. |
| 5.1 | Analyse: Convey mathematical, statistical and financial models clearly and fluently, in high quality written form appropriate for their audience. |
| 5.2 | Synthesise: Develop and communicate complex solutions to real world problems. |
| 5.3 | Evaluate: Prepare and deliver advanced, professional presentations to different audiences to convey problem statements and solutions and place the work in the context of other scholarly research. |
| 6.1 | Analyse: Use ethically appropriate and respectful practices when applying mathematical knowledge as related to Aboriginal and Torres Strait Islander communities. |
| 6.2 | Synthesise: Acquire cultural awareness for the relevant ethical and respectful practices, when developing community relations. |
| 6.3 | Evaluate: Integrate Aboriginal and Torres Strait Islander knowledges and practices when relevant for applying the results of mathematical analysis |
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