Course Listing

This course will introduce modelling process and basic types of models adopted in natural and social sciences. Models from a range of disciplines will be discussed. Prerequisite: one university course in either of calculus or statistics.

This course will introduce fundamental principles and concepts in the general area of system modelling and simulation. Topics to be covered include the basics of discrete-event system simulation, mathematical and statistical models, simulation design, experiment design, and analysis of simulation output. Prerequisite: AMOD 5610H and the ability to program in at least one computational language.

Various statistical approaches to modelling are illustrated, with an emphasis on the applications of statistics within the social and natural sciences. The course discusses both univariate and multivariate procedures, with particular attention to the latter (e.g., multiple regression, multi-analysis of variance, exploratory factor analysis, confirmatory factor analysis, and path analysis). Prerequisite: As for AMOD-5610H, plus a university course in advanced statistics and some knowledge of SAS, SPSS or an alternative statistical application package.

Cross-listed: ENLS-5015H

This course will introduce the student to the statistical programming language R. A wide range of topics will be covered, from data frames and functions to regression and statistical analysis. Emphasis is on visualization and statistical modelling to provide relevant applications for students to graduate research.

Discipline-specific courses in the home department. These may be given by the research supervisor in a reading/project course format.

Big Data applications are pervading more and more aspects of our life, encompassing commercial and scientific uses at increasing rates as we move towards exascale analytics. Examples of Big Data applications include storing and accessing user data in commercial clouds, mining of social data, and analysis of large-scale simulations and experiments such as the Large Hadron Collider. In this course, students from a variety of disciplines will be introduced to the challenges and opportunities in this field, with the goal of providing them with theoretical and hands-on experience in the area of Big Data Analytics.

High Performance Computing is the use of advanced computer architectures to solve problems which require significant processing power, memory access, or storage. Core topics include advanced computer architectures, programming for shared and distributed memory machines, networking issues, caching, performance evaluation and parallel algorithms. Topics are supplemented with case studies. Excludes COIS 4350H.

An introduction to the principles of data mining. Topics to be covered include an overview of existing work in data mining with a special focus on applications in astronomy, sampling mechanisms, the statistical foundations of data mining, the problem of missing data, and outlier detection. We will discuss classification techniques such as Support Vector Machines, Neural Networks, and Decision Trees, as well as clustering techniques including k-means, self-organizing maps, and the Expectation Maximization algorithm. Furthermore, the course includes a practical component using open source software. Excludes COIS 4400H.

Basic mathematical theory and computational techniques for how financial institutions can quantify and manage risks in portfolios of assets. Topics include: mean-variance portfolio analysis, the capital asset pricing model and Value at Risk (VaR).

This course will integrate economic and financial market theory, applied mathematics, and probability and statistics to study econometric methods that are designed to deal with the unique features and characteristics of financial market data. Topics will include multiple regression, time-series analysis, time-varying volatility models, switching models, and limited dependent variable models.

This course covers commonly used numerical techniques for solving differential equations including adaptive, multi-step and finite difference methods. Numerical stability, convergence and the issue of solution consistency would also be covered. Computational implementation will be emphasized with Matlab/Octave.

One of the requirements to complete the Big Data Analytics M.Sc. program is that the student must complete a research project. Each student independently studies an area of Big Data Analytics under the guidance of a faculty supervisor, culminating in a research paper and a final presentation on the topic. A grade will be assigned based on the research project.

One of the requirements to complete the Financial Analytics MSc program at Trent University is that each student enrolled in the program must do a research project in Financial Analytics. Each student independently studies an area of Financial Analytics under the guidance of a faculty supervisor, culminating in a research paper and a final presentation on the topic. A grade will be assigned based on the research paper and the presentation.

Each student makes one presentation per year on his/her research, with emphasis on the assumptions, methodology and analysis of the models used. These presentations are attended and graded by her/his Supervisory Committee. Attendance is compulsory. The course will be given a pass/fail grade based on the presentations, attendance and participation by the student. This course represents the first of two presentations and is expected to be about 10-15 minutes in length.

As with AMOD 5901H, this course represents the second of two presentations required by each student in the program on his/her research. The length of this presentation is expected to be about 25 minutes. As with the first presentation, it will be attended and graded by her/his Supervisory Committee. Attendance is compulsory. The course will be given a pass/fail grade based on the presentations, attendance and participation by the student.

This course is one of the courses offered to students in the new course-based MSc programs, to complement the seminar presentations for students in the thesis stream.