Big Data Analytics Stream: Applied Modelling and Quantitative Methods

This interdisciplinary thesis-basedBig Data Analytics Stream: Applied Modelling and Quantitative Methods option from Trent University offers fresh perspectives for students pursuing an M.A. or M.Sc., bringing together scholars from across economics, computer science, math, and psychology to exchange ideas and share knowledge.

Collaborate on individualized, interdisciplinary research with faculty members in a wide range of disciplines, including Anthropology, Biology, Business Administration, Chemistry, Computer Science, Economics, Geography, Humanities, Mathematics, Physics & Astronomy, Philosophy, or Psychology. This demanding research Big Data Analytics Stream: Applied Modelling and Quantitative Methods program from Trent University builds a solid foundation of modelling skills that will prepare graduates interested in pursuing a Ph.D. program in the discipline of their choice.

Key Facts

This new 12-month, course-based M.Sc. program covers a wide array of data science topics, aiming to equip graduates with the tools and techniques needed to handle and analyze complex data sets across various scientific and social science fields. Students will complete a research practicum during their final term of study ensuring they gain practical experience essential for their academic and professional development.

Trent is one of the first universities in Canada to fill the training gap for professionals in data science. This stream equips you with the tools and techniques required to analyze complex data sets, with emphasis on practical skills in visualization, data mining, cloud-based approaches and parallel programming skills.

Complete this skills-boosting program in 16 months and prepare for in-demand careers, including chief data officer, data scientist, data solutions architect, business manager and business analyst.

Learn theory alongside practical skills as you prepare for a career in equity analysis, financial forecasting, investment banking, commercial banking, corporate planning, portfolio management, pension fund management and other opportunities in the financial sector.

Courses included:

• Mathematical aspects of modelling

• Computational aspects of modelling

• Statistical Aspects ofModelling

• Data Visualization

• High performance computing

• Data Mining