This course will build on the tools, skills, and concepts developed in the first half of the program. As an applied course, students will be expected to routinely perform accounting-based empirical analysis by using the analytics skills they have learned (e.g., SAS, R, and Python). Students must practice their ability to formulate appropriate empirical research questions in the context of the business problem or opportunity. Specifically, they will first learn how to approach and appreciate accounting information and then take advantage of the rich accounting and finance dataset to help businesses solve various problems or enhance corporate profitability. At Rotman, we have an abundance of financial accounting data including COMPUSTAT, CRSP, and IBES to address a large variety of business, finance, and accounting questions. The course has four modules: 1) understanding accounting information, 2) use of financial information in the equity market, 3) use of financial information in the debt market, and 4) use of disclosure.

This course covers financial institutions, financial markets, and the management of different types of risk. It explains the types of funds that are created as investment vehicles and how measures such as value at risk and expected shortfall are used. It covers market risk, credit risk, operational risk, liquidity risk, and model risk and discusses the way the financial landscape is changing. The course will include a major NLP case study.

This course will expose the learner to a broad range of technical skills that are required to prepare data for advanced analysis. Using a combination of theory and practical exercises and case studies, the learner will develop the data acquisition and preparation skills that are a necessary prerequisite to applying advanced statistical modelling, data mining techniques, or machine learning algorithms to their data.

This course will introduce the students to a diverse collection of big data techniques. These techniques are often aimed at identifying and quantifying various structures in the data (e.g., What are the key similarities between certain business units with respect to customer satisfaction? What are the characteristics of important customer segments?). Model validation and effective communication of model-based results will be stressed. The course will employ a "white-box" methodology, which emphasizes an understanding of the algorithmic and statistical model structures.

In this course, we will learn how to structure, analyze, and solve business decision problems using Excel spreadsheets. We will focus on problems involving decision-making and risk analysis. The emphasis of the course will be on systematic, critical and logical thinking, and problem solving using spreadsheets as our primary tool. We will start with the basic techniques of good spreadsheet modeling and organization, and proceed to introduce a variety of modelling techniques and approaches. All along, we will critically think on how to interpret the results of our analysis process in the context of decision-making. These will be illustrated by building and analyzing problems in finance, marketing, and operations. While the underlying concepts, models, and methods of this course are mathematical in nature, we will develop them on the more intuitive and user-friendly platform of spreadsheets, always focusing on the ideas and insights, rather than the underlying mathematical details. The spreadsheet approach to problem solving is more accessible to managers, as they often find spreadsheets a natural medium for organizing information and performing "what if" analyses. We will study how to use Excel and various add-ins to perform such analyses and interpret them. We will study three specific techniques: optimization, decision trees, and simulation. The usage of these techniques in practice can improve the decision making process in many situations.

In this course, students will learn decision making in different service industries including education, healthcare, retail, and car sharing using analytics and modern computer languages (Python).

Students will learn how to design experiments effectively and apply appropriate hypothesis testing to confirm statistically significant variables, how to handle imbalanced data sets and develop appropriate predictive models to optimize planning, how to implement text mining and natural language processing to predict sentiment and emotion, and how to use simulations analysis to estimate waiting time in support of their management.

This content will be introduced to students with cases and data sets that permit students to experience the different tools and improve their confidence in using analytical tools appropriately in the context of real-world problems in the service industry.

Operations and supply chain management functions are heavy analytics users in a number of industries. This course will focus on a selection of important supply chain management decision problems.

For these decision problems, the course will focus on how to appropriately combine data, modelling, analytical techniques, and tools to systematically 1) understand, structure, and formulate the problem; 2) evaluate key performance metrics under various policies; 3) optimize key performance metrics; and 4) interpret and communicate the results.

The course will be composed of short (2-3 week) modules ("colloquia") taught by practitioners in the related fields. The course will provide students with skills that will be instrumental to achieving career success in data science. The course will start in the Fall session of the MMA program and continue through the Winter session. The goal of this course will be to expose students to some current topics and themes in analytics.

The course will be composed of short (2-3 week) modules ("colloquia") taught by practitioners in the related fields. The course will provide students with skills that will be instrumental to achieving career success in data science. The course will start in the Fall session of the MMA program and continue through the Winter session. The goal of this course will be to expose students to some current topics and themes in analytics.

In this practicum course, you will learn how to apply model- and data-based decision making to a problem that a real organization currently faces. These problems are not only more realistic than the problems you will face in individual courses, they are more holistic. Rather than focusing on an individual component of an analytical task, they involve all steps, from understanding the underlying managerial issues, to structuring an analytical data view, to effectively presenting your findings and proposed implementation plans. The problems you will deal with are also messier than the ones you encounter in class, in the sense that they may not initially be well-defined, may span functional areas, may invite competing approaches and explanations, and may lack ideal data.

The goal of this course is to introduce the students to key ideas about data-intensive business decision-making. The ideas explored in the course include: 1) Understanding that the questions a business needs answered precedes the collection and analysis of data. 2) The difference between what the data "say" and what the data "mean." 3) Understanding and measuring randomness and its implications. Different sources of randomness (inherently random outcomes vs. measurement errors). 4) Introduction to standard questions and analyses that businesses need to address. 5) Understanding traps and biases in the data and their implications on the analysis. 6) Difference between various modelling approaches. 7) In sum, this course is designed to get you excited about how you can use data and analysis to help a business make better decisions.

This course provides a hands-on introduction to the wide variety of models and techniques used in predictive analytics, including linear and non-linear regression models, classification algorithms, machine-learning techniques like SVM and reinforcement learning, and causal inference. There will be an emphasis on conceptual understanding and interpretation of the models, so that students can interpret the results of these techniques to support effective decision-making. The course will be complemented by many hands-on exercises using the R programming language.

This course illustrates how managers can use data from various sources (sales data, historic consumption data, transactions data, marketing effectiveness data) in making more effective business decisions. We will understand the basic principles of data driven marketing in several industries. Applications will range from targeting decisions, segmentation decisions, customer relationship management (CRM), resource allocation, retention and loyalty programs.

The forces and dynamics of today's market are making the marketing task more complex and competitive. In this context, a successful marketing strategy involves complementing the basic elements of marketing mix, with analyzed data, and appropriate models and simulations incorporated in a consistent and professional way, elements of marketing strategy as well as the skills needed to make intelligent use of marketing data in making recommendations about marketing strategies. These are learned through a combination of lectures, cases, and "hands-on" exercises with actual business data. The course is designed to equip the student with practical "know how," which can be used immediately on the job. Students gain a working knowledge of segmentation, targeting and positioning, conjoint analysis for product design, forecasting the demand of a new product, pricing analysis, channel design, allocation of resources amongst different promotional vehicles, and an introduction to digital marketing analytics.

More than 80% of human work in organizations is carried out in groups and teams, and around 80% of an executive's time is spent on communicating. Generative AI tools (and, in particular, Large Language Models and Large Symbol Models capable of interacting with people in textual or symbolic forms) will likely be a complement or substitute for significant components of the individual work people still do in organizations within five years. We are aiming to help each participant to become more skilled at those skills that are uniquely human in an executive setting — by becoming an astute and informed observer, evaluator and designer of her/his own social, relational, and communicative interactions, and by becoming informed users of Large Language Models to generate useful prototypes, blueprints for communications in the specific genres of business, which include emails, memoranda, presentations, and pitches.

This course serves as an introduction to the functional areas of business: Marketing, Operations, Accounting, Finance, Organizational Behaviour, and Strategy. Our focus will be on learning the main concepts of each of these areas and how they relate to each other. We will also consider the role of analytics in these areas in creating descriptive and prescriptive models to aid in decision making. As such, the modules in each area will lay the foundations for further study of these topics throughout the year. Students will participate in case analyses and discussions, group activities, and a group presentation.

Course content to be decided by students in consulation with the instructor. Please consult the Collaborative Specialization's website for details on the annual offering.

Course content to be decided by students in consulation with the instructor. Please consult the Collaborative Specialization's website for details on the annual offering.

This reading-intensive seminar serves two purposes: 1) to introduce students to a range of contemporary disciplinary perspectives on the region of South Asia, and 2) to critically examine the formation of knowledge around the concepts of globe, area, and region. We will focus in particular on the ways in which colonialism, anti-colonial nationalisms, and postcolonial imperial formations have shaped disciplinary categories and institutions, always mediating interpretations of South Asia's past, present, and future. Although units have been organized roughly around disciplinary nodes, certain themes such as gender, religion, postcolonial modernity, community, nationalism, capitalism, and the politics of translation will cut across our readings and discussions.

Course content varies in accordance with the interest of the instructor. Please consult the Collaborative Specialization's website for details on the annual offering.

This course serves as the core requirement for the collaborative specialization in Sexual Diversity Studies. It covers important theories, methods, and historical movements in queer, trans, and sexuality studies across the disciplines. It approaches sexuality studies through an intersectional lens by examining how colonialism, settler colonialism, migration, class structure, and neoliberalism shape and are shaped by gender and sexual minorities locally and globally.

This postgraduate seminar gathers technē and technologies of trans livingness ecologies of thought, and aesthetic production (i.e., artefacts, performance, coalitional social movements, texts, archival formations). Each week stages a conversation in the interstices of theory, praxis, and other cosmologies, including between "borders and transitions," "liberation and emancipation," "identification and identity," "atmospheres and imaginaries," pronouns and prefixes (e.g., the "non" in nonbinary), "rights and autonomy," "racial logics and colonial grammars," "poetics and practices." Our study encounters legal writing and public policy; ethnography; black studies, performance; historiography; critical theory; dominant archives and technologies of dispossession. Informed by interventions from those most impacted by a nexus of violence, we will produce creative work and practical propositions that develop collaborative responses and our own transmedia and transdisciplinary tools for navigating the present political conjuncture.

Ce cours s’intéresse aux méthodes narratives en éducation et tout particulièrement à l’approche méthodologique des récits, des contre-récits et des récits alternatifs. Nous naviguons dans le quotidien à partir de catégories de classement qui influencent notre rapport au monde et aux autres et à partir desquelles se perpétuent les injustices scolaires et sociales. Ce dispositif méthodologique vise à remettre en cause les représentations sociales dominantes et à constituer ainsi un espace de découverte, de réflexion, de dialogue et de transformation. Nous abordons l’étude de ce thème à partir de la sociologie de l’éducation où l’élément pivot sera celui de la socialisation, c’est-à-dire de la prise en considération de l’influence et des contraintes qu’exerce le social dans les parcours de vie. Sera abordée dans ce cours la quête de sens dans les enquêtes narratives, de même que les fondements épistémologiques du récit, l’entretien biographique, l’analyse et la mise en mots de récits, les questions d’éthiques, la présentation de contre-récits, et enfin, la proposition de récits alternatifs. En s’inspirant d’études variées, le cours met en valeur le potentiel de transformation que recèlent les récits de vie dans les divers usages dont ils font l’objet.

Ce séminaire s’intéresse au sens que les élèves donnent à l’expérience scolaire ainsi qu’aux facteurs de différenciation sociale qui influencent leur parcours dès l’entrée à l’école. L’étude des inégalités est un thème prolifique en sociologie et dans les sciences de l’éducation. Il s’agit d’un enjeu qui est toutefois peu abordé à partir des voix d’enfants dans les facultés d’éducation malgré les avancées en recherche, recensées sur plusieurs décennies. Le séminaire offre l’occasion de se familiariser avec la sociologie de l’enfance. Il met en lumière la multiplicité des parcours de vie d’enfants et, pris collectivement, d’une pluralité d’enfances. Sont aussi abordées des enquêtes réalisées par des sociologues de la jeunesse au Québec, au Canada et en Europe. Ce séminaire aborde l’enfance et la jeunesse par le prisme de l’éducation et de la question des inégalités. Les thèmes à l’étude incluent l’émergence de nouvelles perspectives sur l’enfance, l’étude de la différenciation sociale entre enfants, les concepts clés sur l’enfance et la jeunesse, tels que la parole d’enfants, la capacité d’action, la participation, l’âge, les transitions et la temporalité, la production des inégalités par les enfants, le rôle de l’enfant dans la production de connaissances, et enfin l’équité, l’inclusion et la prise de parole d’enfants.

This course investigates knowledge, knowing, and knowing subjects as they are represented in modern and postmodern educational theory and practices. The course is designed to facilitate educators' self-reflection on questions of learning and teaching, constructions of knowledge and knowers, and the implications of power/knowledge. Selected topics include: the impact of constructivism on teaching; problems of epistemic dominance and marginalization (Whose knowledge counts?); and representations of learning (styles; ability/disability).

This course is an overview of the field of philosophy of education. It focuses on selected major thinkers, such as Plato, Rousseau, Wollenstonecraft, Dewey, Peters, and Martin, with attention given both to classic texts and to contemporary developments, critiques, and uses of ideas from these texts. Emphasis is placed on the kinds of epistemological, ethical, and political questions that comprise the core of philosophy of education and that need to be addressed to the classic and contemporary literature.