MA/MS in Applied Modelling & Quantitative Methods: Big Data Financial Analytics is a master's degree in Data Science & Analytics programme taught at Trent University in Peterborough, Canada.
At a glance
Degree:Master's Degree
Field:Data Science & Analytics
We have no tuition fees or an application deadline on record for this programme yet — see the official programme page.
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Last reviewed September 2026 ·
Program details
Overview
Applied Modelling & Quantitative Methods: Big Data Financial Analytics is a course-based Master of Science stream within Trent University's interdisciplinary AMOD program, which applies modelling techniques and theory to problems in the natural and social sciences. The program brings together graduate students who are actively applying quantitative, computational, mathematical, or statistical modelling techniques across disciplines including Biology, Business Administration, Chemistry, Computer Science, Economics, Geography, Mathematics, Physics & Astronomy, and Psychology, and it is discipline-based rather than a program in applied mathematics. The Big Data Financial Analytics stream applies this general AMOD modelling approach to financial data problems, and is one of three course-based MSc streams AMOD offers alongside Big Data Analytics and Data Science & Analytics, with a thesis-based MA/MSc stream also available. All AMOD streams share two core objectives: teaching the fundamental analytical modelling techniques needed for research across a wide range of quantitative fields, and fostering cross-disciplinary collaboration and communication skills through contact with researchers from other fields.
Requirements
Minimum GPA:77.00 / %
Admission Requirements: The minimum requirement for admission to a Master's program is a Bachelor's degree, in an Honours program or the equivalent, from a recognized university. Applicants must have achieved an average of at least B+ (77%) or equivalent in the work of the last completed four semesters or the last two completed undergraduate years (full-time equivalent).