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.

Mathematical approaches to modelling are illustrated, with the emphasis on the methods rather than on the mathematical details. The topics include analytical modelling and computer simulation of dynamic processes, decision making, forecasting, probabilistic analysis, based on case studies from biology, geography, physics, economics, and social sciences. Prerequisite: any university course in mathematics or physics, and working knowledge of a programming language.

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: the ability to program in at least one computational language. Excludes AMOD 5810H.

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: 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.

This course is designed to familiarize graduate students in the data sciences with suitable genres of academic writing and communication. Part of the course will also focus on information literacy in the field, to help students better locate and evaluate the information they will need to utilize in their research. Other parts of the course will provide students with a suitable rhetorical approach to writing in scientific disciplines. Students will have opportunities to improve their existing writing and communication skills in these genres and develop a deeper comprehension of effective communication. The course will also provide opportunities to learn how to be an effective reviewer and editor, since students will be asked to evaluate the written communication of others.

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

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.

Data visualization is a main step in the analysis of data in a wide range of scientific research areas as well as business applications. We will discuss general approaches and tools, and techniques for the visualization of various types of data, including spatial data, graph data, and time series data. Excludes COIS 3510H.

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.

This course introduces database systems and their use in the management of large quantities of data. The objectives are to gain an understanding of the information modeling and representation, the essential concepts, principles, techniques, and mechanisms for the design, analysis, use, and implementation of computerized database systems, and to gain experience in implementing and accessing relational databases using MySQL. At the end of this course, a student will be able to understand and apply the fundamental concepts required for the design, use and optimization of database management systems.

Introduction to Data Science develops a solid foundation in the main concepts of data science and programming in Python. Core topics include repetition and selection structures, algorithm design techniques, file types, Big Data, Data Mining and Data Visualization.

Introduction to the concepts, techniques and applications of linear programming and discrete optimization, Topics include the simplex method, duality, game theory and integer programming.

This course covers the basic mathematical theory and computational techniques used to price a variety of stock and interest rate options using both discrete time models, such as binomial probability trees, and continuous time models, such as the Black Scholes model. Elementary concepts from stochastic calculus will be introduced. Computational techniques will be emphasized with implementation of models in Maple and Excel.

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 introduces core concepts central to financial management and firm value maximization. You will learn the basic methods of valuing corporate securities, estimating cash flows, and making investment decisions. Introduction to portfolio management theory, cost of capital, and raising capital will round out the course.

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 Big Data 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.

Each student in a course-based stream will present his or her work on the research project, with emphasis on the assumptions, methodology and analysis of the models used. Attendance is compulsory. The course will be given a pass/fail grade based on the presentations, attendance and participation by the student. The presentation is expected to be about 10-15 minutes in length.