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Students
Tuition Fee
AUD 1,002
Start Date
Medium of studying
Duration
24 months
Program Facts
Program Details
Degree
Masters
Major
Statistics | Applied Statistics | Econometrics
Area of study
Mathematics and Statistics
Timing
Full time
Course Language
English
Tuition Fee
Average International Tuition Fee
AUD 1,002
Intakes
Program start dateApplication deadline
2024-02-05-
2024-07-22-
About Program

Program Overview


The Master of Data Science in Quantitative Finance at UTS combines quantitative finance with data science and statistical modeling. It equips students with a deep understanding of financial theory and data science techniques, preparing them for careers in the financial industry or academia. The program includes core subjects in probability, financial instruments, econometrics, and machine learning, complemented by elective subjects in data science, quantitative finance, and risk management.

Program Outline

Degree Overview:


Overview:

The Master of Data Science in Quantitative Finance at UTS is a postgraduate program that combines subjects from the internationally acclaimed UTS Master of Quantitative Finance with specialist study in data science and statistical modeling. The program is designed for aspiring and established quantitative finance professionals.


Objectives:

  • To provide students with a deep understanding of the theoretical foundations of quantitative finance and data science
  • To develop students' skills in applying quantitative methods to real-world financial problems
  • To prepare students for careers in the financial industry or academia

Program Description:

The Master of Data Science in Quantitative Finance comprises 96 credit points of core subjects. The program is typically completed in two years of full-time study or four years of part-time study. The core subjects cover a range of topics, including:

  • Probability theory and stochastic analysis
  • Financial market instruments
  • Statistics and financial econometrics
  • Interest rates and credit risk models
  • Fundamentals of derivative security pricing
  • Risk management
  • Machine learning: mathematical theory and applications
  • Advanced Bayesian methods
  • Mathematical research project

Outline:

The core subjects are complemented by a range of elective subjects, which allow students to tailor their program to their individual interests. Elective subjects are available in the following areas:

  • Data science
  • Quantitative finance
  • Financial mathematics
  • Risk management

Assessment:

Students are assessed through a variety of methods, including:

  • Assignments
  • Quizzes
  • Exams
  • Presentations
  • Projects
  • The assessment criteria vary depending on the assessment method. However, all assessments are designed to assess students' understanding of the course material and their ability to apply quantitative methods to real-world problems.

Teaching:

The Master of Data Science in Quantitative Finance is taught by a team of experienced academics and industry professionals. The program is taught through a combination of lectures, tutorials, and workshops. The program also includes a strong emphasis on practical experience. Students are required to complete a research project, which involves applying quantitative methods to a real-world financial problem.


Careers:

Graduates of the Master of Data Science in Quantitative Finance are in high demand by leading financial institutions, management consulting companies, energy and mining companies, regulatory bodies, government organizations, and other organizations seeking advanced data science and quantitative finance expertise. Potential career paths include:

  • Quantitative analyst
  • Data scientist
  • Data analyst
  • Quantitative structurer
  • Quantitative developer
  • Forecaster
  • Trader
  • Financial engineer
  • Market risk analyst
  • Credit risk analyst
  • Data engineer
  • Data modeler
  • Investment analyst
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