Ecosystem science provides the scientific foundation to support the sustainable management of diverse ecosystem services including provisioning, regulating, cultural, and supporting services. A deep understanding of interactions between different ecosystem services and impacts of different human and natural disturbances on ecosystem’s health is critical to design and implement sustainability management practices. This course provides an in-depth understanding of ecosystem science and ecosystem-based management systems. The course focuses on terrestrial and aquatic ecosystems. The course covers ecosystem structures and functions, ecological energetics (primary production, secondary production, and consumer energetics), biogeochemistry (carbon, nitrogen, and phosphorus cycles), nutrients and pollutants in ecosystems, ecosystem budgets, ecological restoration, ecosystem-based management, and adaptive management.

Sustainability ultimately depends upon human behaviour. At both individual and organizational levels, the actions that people take have profound social, economic, and environmental consequences. These consequences are manifest both in the immediate present and even more strongly in the experience of future generations. In this course, we will explore the human factors that influence the adoption of sustainable or unsustainable practices. Why do people prefer immediate gratification instead of longterm gains? How can we incentivize sustainable behaviour? What is the best way to act as an effective change agent within an organization? These questions will be addressed by examining fundamental topics in Psychology and Organizational Behaviour, including motivation, decision-making, and social dynamics. Over the course of the semester, our primary goal is to understand how insights from the behavioural sciences can be used to design and promote sustainable organizational practices.

The course is designed to provide students with a basic understanding of various laws and policies related to the environmental, social, and economic pillars of sustainability that have relevance to a practicing professional sustainability manager. The course commences with an overview of the structure of the Canadian legal system and then divides in two parts. The first part focuses on environmental law and policies. This part covers international agreements, such as Global Programme of Action for Sustainable Development (Agenda-21), Kyoto Protocol, Biodiversity Convention, and Future We Want (outcome of Rio+20); Canadian laws, such as Environmental Protection Act, Federal Sustainable Development Act, Federal Sustainable Development Strategy and Bill C-45; and Ontario's laws such as Environmental Protection Act, Environmental Assessment Act, Green Energy Act, and Open for Business Act. The second part focuses on laws related to social and economic pillars and covers the Canadian laws of torts, contracts, sole proprietorship, partnerships, corporations, bankruptcy.

Why are some organizations more successful than others? Can we identify the roots of superior performance and devise ways to leverage the drivers of success? Strategic management enables us to understand the attractiveness and dynamics of industries, the drivers of competitive advantage, as well as the value propositions required to deliver and capture value. In the process of creating, delivering, and capturing value, organizations also affect the well-being of society. As such, strategic management also requires an assessment of the social responsibility and environment sustainability provided by organizations through their strategies. Strategic management is of relevance not only to profit-seeking corporations, but also for non-profit organizations, public agencies, supra-national organizations and countries as a whole. This course examines how organizations select markets, compete with each other, and organize themselves internally to produce successful outcomes. As part of this, the implications for sustainability are an integral consideration.

The course is designed to develop an integrative understanding of creating and managing a sustainable enterprise. In the first part of the course, basic concepts related to integrative and systems thinking, key-features of sustainable enterprise, organizational design, and strategic management planning will be discussed. In the second part, students (in a group of two or three students) will work on a project related to design and management of sustainable enterprise under the supervision of course instructors. The focus of projects will be on demonstrating integration, application, and innovation skills to address challenges faced by an organization with a goal to become a sustainable enterprise.

SSM1100Y is a core (mandatory) course intended to provide students with the opportunity to conduct independent academic research on a topic that falls within the interdisciplinary fields of the MScSM program. It is not meant to be a thesis but should adhere to principles of excellent research. Students work on a research project in their area of interest, under the supervision of a faculty member with expertise and research interests in the topic area. Through semi-regular meetings with the course instructors, and in collaboration with the students' supervisor, students will work to identify potential research questions, apply methodological skills and research skills appropriate for addressing the research question, and conduct primary or secondary research. Finally, students write up their results in a full-length research report, and present their findings in an open forum to members of MScSM Program as well as interested members of IMI and the wider UTM community. (Management Credit)

SSM1100Y is a core (mandatory) course intended to provide students with the opportunity to conduct independent academic research on a topic that falls within the interdisciplinary fields of the MScSM program. It is not meant to be a thesis but should adhere to principles of excellent research. Students work on a research project in their area of interest, under the supervision of a faculty member with expertise and research interests in the topic area. Through semi-regular meetings with the course instructors, and in collaboration with the students' supervisor, students will work to identify potential research questions, apply methodological skills and research skills appropriate for addressing the research question, and conduct primary or secondary research. Finally, students write up their results in a full-length research report, and present their findings in an open forum to members of MScSM program as well as interested members of IMI and the wider UTM community. (Science Credit)

The graduate students in the Master of Science in Sustainability Management are required to complete a summer internship/co-op placements in order to graduate. As part of their course of study, they must undertake a placement of 10 to 16 weeks in length, from May to August, in an industry of their choosing. Placements may be in-person, remotely or in a hybrid mode; students and host employers will have the full support of our Program Office throughout the term. Suitable positions can be in any area that relates to sustainability — including ESG, reporting, strategy, CSR, engagement, community partnerships, policy, government relations, and more — or in any traditional area of the business that aligns with their skills and interests, including marketing, finance, and beyond.

How is environmental sustainability a profoundly social challenge? This class will explore how environmental problems are deeply intertwined with social problems, just as humans and human institutions are part and parcel of nature. Students will be challenged to integrate sociological insights into efforts toward critically-engaged forms of sustainability management. The course will provide an overview of environmental sociology along with topics such as environmental injustice and toxic exposures, consumption and green capitalism, corporate culture and institutional change, environmental risk and disaster, and several weeks on various dimensions of the climate crisis including the political economy of climate adaptation, denialism, social movements and counter movements, and predicative politics.

The course is designed to develop an understanding of: (i) relationship between sustainability and marketing; (ii) linkages between sustainability concerns and people’s behaviour including their behaviour in markets; (iii) differences between the principles of conventional marketing and sustainability marketing; (iv) sustainability marketing values and strategies; and (v) applications of sustainability marketing concepts and tools to a range of profit and non-profit organizations. The course will include a range of topics such as evolution of marketing, sustainability, and sustainability marketing; elements of sustainability marketing and corporate social responsibility; challenges and opportunities for sustainability marketing; sustainability and people's (consumer's) behaviour; harnessing people's behaviour for sustainability; sustainability marketing values and objectives; sustainability marketing strategies; sustainability marketing mix including customer solutions, communication, cost, and convenience; innovations and sustainability marketing; future directions of sustainability marketing; and applications of sustainability marketing.

Many students finish their master's degree, but know little about how business decisions are made, how they should be made, and how to have an optimal impact on corporation executives and their decisions. This course is designed to provide the background understanding of business and ethics to ensure that students can argue effectively and ensure that your master's level knowledge is able to make a significant favourable impact on employers. In this course, students will develop an understanding of: 1) the ethics of sustainability and innovation, 2) business governance and ethics, 3) how business views sustainability, 4) how to influence corporate strategy and decision making through business ethics, and 5) important current and future topics and issues in sustainability and innovation ethics. The focus of the course will be practical and will build upon a historical understanding of ethical developments to offer students a perspective on current practices as well as future prospects.

This is a directed studies courses, and therefore the course content, for every offering, will be decided by the specific instructor and student. The course will be open to all areas related to sustainability management such as strategy, managerial economics, managerial accounting, marketing, organizational behaviour, environmental science, ecosystem science, sustainability ethics, and sustainability policy and law. The focus in this course will be on applied topics related to any of these areas such as applications of the principles of strategic management, organizational behaviour, managerial accounting, managerial accounting, and marketing to design/reform/manage sustainability practices of an organization. This course can be used as either a Management or Science elective, depending on the topic of study. At the time of registration, the Program Director will determine which concentration course will apply to depending on the course content, as agreed upon by the instructor, student, and Director.

This is a directed studies courses, and therefore the course content, for every offering, will be decided by the specific instructor and student. The course will be open to all areas related to sustainability management such as strategy, managerial economics, managerial accounting, marketing, organizational behaviour, environmental science, ecosystem science, sustainability ethics, and sustainability policy and law. The focus in this course will be on applied topics related to any of these areas such as applications of the principles of strategic management, organizational behaviour, managerial accounting, managerial accounting, and marketing to design/reform/manage sustainability practices of an organization. This course can be used as either a Management or Science elective, depending on the topic of study. At the time of registration, the Program Director will determine which concentration course will apply to depending on the course content, as agreed upon by the instructor, student, and Director.

As a special topics course, the theme and content will vary with every offering. Some examples of course topics include Climate change, Biodiversity, Gender Equity, Poverty Alleviation, Greenhouse Gas Emissions and Corporate Social Responsibility. This course will explore the diversity of approaches, ideas and concepts related to the theme. Students will be exposed to both social and natural science perspectives through in-class lectures and discussions, as well as regular guest lecturers. A major end-of-term project requiring the application of theories and methodologies from their chosen area of focus (science of management/social science) is required.

This course is a statistics course for life and social science graduate students. Please consult the department for further details.

This course is intended to graduate students in disciplines other than statistics whose studies involve research design and statistical data analysis. Topics include vocabulary of data analysis and principles of research design, significance tests, Type 1 and 2 errors, power and sample size, simple and multiple linear regression, ANOVA, analysis of correlated data (repeated measures), random effects models, introduction to R and computer-intensive methods (permutation, bootstrapping, simulation), and further topics depending on interests of students or instructor.

Practical techniques for the analysis of multivariate data; fundamental methods of data reduction with an introduction to underlying distribution theory; basic estimation and hypothesis testing for multivariate means and variances; regression coefficients; principal components and the partial multiple and canonical correlations; multivariate analysis of variance; classification and the linear discriminant function. The use of R software should be expected.

Discrete and continuous time processes with an emphasis on Markov, Gaussian, and renewal processes. Martingales and further limit theorems. A variety of applications taken from some of the following areas are discussed in the context of stochastic modeling: Information Theory, Quantum Mechanics, Statistical Analyses of Stochastic Processes, Population Growth Models, Reliability, Queuing Models, Stochastic Calculus, Simulation (Monte Carlo Methods).

Data acquisition in the environmental, physical, and health sciences are increasingly spatial, and novel in the sense that specialized methods are required for analysis. This course will cover different types of spatial and spatiotemporal data and their analytic methods. Students will learn a variety of advanced techniques for analyzing geostatistical, areal, and point referenced data. Focus will be placed on visualizing spatial data, choosing the correct method for a specific research question, and communicating analytic results clearly and effectively.

A rigorous introduction to stochastic analysis and its applications. Topics include Brownian motion, continuous time martingale, stochastic integration, stochastic differential equations, diffusions, and further topics depending on the interests of the instructor.

Techniques for formulating data science models as optimization problems. Algorithms for solving data science problems including gradient-descent based algorithms and randomized algorithms. Emphasis on scalability and efficiency. Convergence analysis of algorithms. Coverage of both convex and nonconvex optimization.

Modern statistical methods and data analytics are increasingly informing decisions in law, business, medicine, and public life. While the use of statistics to understand social problems is not new, its pervasiveness in society and the scale of available data available opens up a host of new and/or salient moral problems including, for example, fairness, bias, privacy, equality, transparency, accountability, and accessibility. In this course, we will combine material from law and philosophy together with recent work in statistics and data science in order to gain a better understanding of how to intelligibly reason about these problems, and how to responsibly and creatively apply statistical methods to complex social problems. The course will be research/project based and the emphasis will be on using statistics to address complex social problems rather than on memorizing abstract ethical principles for handling or processing data.

The topics will vary year to year and give students the flexibility to examine a diverse range of subjects relevant to applied statistics and data science. This special topics course is repeatable for credit if taken with a different individual topic.

Statistical genetics is an important data science research area with direct impact on population health, and this course provides an introduction to its concepts and fundamentals. We start with an overview of genetic studies to have a general understanding of its goal and study design. We then introduce the basic genetic terminologies necessary for the ensuing discussion of the various statistical methods used for analyzing genetic data. The specific topics include population genetics, principles of inheritance, likelihood for pedigree data, aggregation, heritability, and segregation analyses, map and linkage analysis, population-based and family-based association studies and genome-wide association studies. The flow of the content generally follows that of the "The Fundamentals of Modern Statistical Genetics" by Laird and Lange, and additional materials will be provided. Participating students do not need formal training in genetics, but they are expected to have statistical knowledge at the level of STA303H1 Methods of Data Analysis II or equivalent.

This course will focus on principles and methods of applied statistical science. It is designed for MSc and PhD students in Statistics, and is required for the Applied Paper of the PhD comprehensive exams. The topics covered include: planning of studies, review of linear models, analysis of random and mixed effects models, model building and model selection, theory and methods for generalized linear models, and an introduction to nonparametric regression. Additional topics will be introduced as needed in the context of case studies in data analysis.

The goal of this course is to give an overview of some of the computational methods that are useful in statistics. The first part of the course will focus on basic algorithms, such as the Fast Fourier Transform (and related methods) and methods for generating random variables. The second part of the course will focus on numerical methods for linear algebra and optimization (for example, computing least squares estimates and maximum likelihood estimates). Along the way, you will learn some basic theory of numerical analysis (computational complexity, convergence rates of algorithms) and you will encounter some statistical methodology that you may not have seen in other courses.

This course will consider topics in statistics that have played a role in the development of techniques for data mining and machine learning. We will cover linear methods for regression and classification, nonparametric regression and classification methods, generalized additive models, aspects of model inference and model selection, model averaging, and tree-based methods.

This is a course designed for master's and PhD level students in statistics, mathematics, and other departments, who are interested in a rigorous, mathematical treatment of probability theory using measure theory. Specific topics to be covered include: probability measures, the extension theorem, random variables, distributions, expectations, laws of large numbers, Markov chains. Students should have a strong undergraduate background in Real Analysis, including calculus, sequences and series, elementary set theory, and epsilon-delta proofs.

This course is designed for graduate students in Statistics and Biostatistics. Review of probability theory, distribution theory for normal samples, convergence of random variables, statistical models, sufficiency and ancillarity, statistical functionals, influence curves, maximum likelihood estimation, computational methods.

Statistical inference is concerned with using the evidence, available from observed data, to draw inferences about an unknown probability measure. A variety of theoretical approaches have been developed to address this problem and these can lead to quite different inferences. A natural question is then concerned with how one determines and validates appropriate statistical methodology in a given problem. The course considers this larger statistical question. This involves a discussion of topics such as model specification and checking, the likelihood function and likelihood inferences, repeated sampling criteria, loss (utility) functions and optimality, prior specification and checking, Bayesian inferences, principles, and axioms, etc. The overall goal of the course is to leave students with an understanding of the different approaches to the theory of statistical inference while developing a critical point-of-view.