Image
Master of Science (MSc) – Data Science and Business Analytics
Get ready to take on the management challenges of the digital and artificial intelligence era. Develop expertise in analytics and data science, and acquire the tools to make better decisions, innovate, and be more competitive.
Overview
| Acquisition of concepts for developing models and advanced methods in optimization, modeling, statistics and machine learning/deep learning. |
| Development of skills to address decision-making issues in management: building complex data or optimization models, analytics and problem-solving using programming languages and appropriate software. |
| Learning principles for optimizing business processes. |
| Students can choose the supervised project track to gain real work experience, or explore a specific area of interest in the research-oriented thesis track. |
| Professors renowned in Canada and abroad, in both the academic and business communities. |
| French language courses and the Experience Quebec course can help international students integrate into the Quebec and Canadian job markets. This pathway is offered to most students who choose the supervised project stream. |
Program characteristics
-
Graduate
-
45 credits
A credit is a unit used to assign a numerical value to the workload required for students to meet the objectives of a given course. One credit represents 45 hours of work. Courses in the Québec university system are generally worth three credits each.
- Full-time
- Part-time
- Day
- On-site
- Côte-des-Neiges
Research in your field of study
The IVADO artificial intelligence consortium brings together professional and research teams to develop expertise in the fields of data science, optimization (operational research), and artificial intelligence.
Several professors in this specialization are members of MILA, a world-renowned artificial intelligence research centre located in Montréal.
- Research Chair in Data-driven Decision Making
- Canada Research Chair in Sequential Decision-Making Under Uncertainty
- SCALE AI Research Chair in Artificial Intelligence for Urban Mobility and Logistics
- 2 Canada CIFAR Chairs in Artificial Intelligence
- Centre de recherche mathématiques (CRM)
- Interuniversity Research Centre on Enterprise Networks, Logistics, and Transportation (CIRRELT)
- Group for Research and Decision Analysis (GERAD)
A step towards your future career
Organizations trust the expertise acquired by students with this master’s degree. This is evidenced by examples of proposed supervised projects.
- Data analysis in the context of sentiment analysis
- Creation of forecasting models in a business context: purchases, sales, cancellation, unsubscribing and breakdowns
- Credit risk modeling
- Inventory management optimization
- Customer segmentation in a marketing context
QTEM Masters Program: join a world-class network!
Are you looking to develop skills in analytical and quantitative techniques to support decision-making in an international context? Spend a term at one of the institutions in the prestigious QTEM network and earn your QTEM certification in quantitative technology in economics and management.
Your career outlook
Nearly
100%
placement rate
Expertise sought in many management fields: finance, marketing, logistics, operations management, human resources management, etc.
Positions held by program graduates:
- Applications analyst
- Business analyst
- Business intelligence analyst
- Director of operations
You may also be interested in this other program
You may also be interested in this other program
Already started your program? Check your course list in HEC en ligne > Academic Progress
This specialization offers either a thesis or a supervised project stream, for a total of 45 credits over a period of 16 to 24 months.
Select a stream:
Thesis
Thesis
- 7 courses (21 credits)
- Thesis (24 credits)
3 specialization courses
-
MATH 60606AFundamentals of OptimizationSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3The Simplex algorithm and key optimization principles in Linear Programming are explored, including sensitivity analysis and duality. In Integer Linear Programming, basic concepts, logical relations and their applications are studied, along with branch-and-bound and branch-and-cut algorithms. The Non-Linear Programming section covers convexity and complementary conditions, as well as gradient- and Newton-based algorithms. Finally, foundational concepts of classical heuristics and metaheuristics are introduced. The course develops essential skills for solving complex optimization problems in various fields.Themes covered
Linear programming
The simplex algorithm
Sensitivity analysis and duality
Integer programming and its applications
Branch-and-bound and branch-and-cut algorithms
Basic notions and algorithms in non-linear programming
Convexity KKT Conditions and Optimality
Gradient- and Newton-based algorithms
Heuristics and metaheuristicsSee the course webpageImportant notesCourse in French : MATH 60606 -
MATH 60629AMachine Learning I: Large-Scale Data Analysis and Decision MakingSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3Massive datasets are now common and require scalable analysis tools. Machine learning provides such tools and is widely used for modelling problems across many fields including artificial intelligence, bioinformatics, finance, marketing, education, transportation, and health. In this context, we study how standard machine learning models for supervised (classification, regression) and unsupervised learning (for example, clustering and topic modelling) can be scaled to massive datasets using modern computation techniques (for example, computer clusters). In addition, we will discuss recent models for recommender systems as well as for decision making (including multi-arm bandits and reinforcement learning). Through a course project students will have the opportunity to gain practical experience with the analysis of datasets from their field(s) of interest. A certain level of familiarity with computer programming will be expected.Themes covered
1) distributed and parallel computation
2) Supervised learning
3) Non-supervised learning
4) Reinforcment learning
5) Recommendations system
6) Sequential decision making
See the course webpageImportant notesCourse in French : MATH 60629 Prerequisite(s) : MATH 60600(A) and MATH 60602. Only for specializations other than Data Sc. and Bus. Analytics. The PhD course MATH 80629A is now a MSc course -
MATH 60604AStatistical ModellingSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3The course will focus on regression models, including linear models, generalized linear models and models for longitudinal data with random effects and correlation structures. For each of these models, basic inference principles will be covered, notably hypothesis testing and estimation procedures.Themes covered
Basic principles in inference and statistical modeling
Linear models
Generalized linear models
Models for longitudinal data and correlated data
Introduction to survival analysisSee the course webpageImportant notesCourse in French : MATH 60604 This course is not credited in the "Intelligence d'affaires" specialization.
Cours mutuellement exclusif(s) : MATH 60619(A) Vous ne pouvez pas vous inscrire à ce cours si vous avez postulé ou réussi le cours MATH 60619(A), ou si le cours MATH 60619(A) fait partie de votre structure.
1 MSc program course
-
GEST 60401AComparative Perspectives on Societal Issues and Business OrganisationsSubjectGestionProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3The course is based on the reading and discussion of fundamental texts in social science, more particularly in economics, sociology, anthropology and history. The objective is for students to apprehend major social and environmental issues and to see business organizations from a global perspective. The course thus aims at supporting students in becoming critically minded and ethically oriented actors of society and of the business world, in order to address business ethics issues and major social and environmental issues in a more informed and reflexive way. A key aspect of the course is coteaching: two instructors - one from the Department of applied economics, one from the Department of management - coteach the course, thereby providing comparative perspectives on major social and environmental issues, business organizations, and management.Themes covered
- The emergence of Western modern society
- The model of modern Western society and its social and environmental implications
- The economic and social foundations of business organizations
- The individual and collective driving forces of business organizations
- The business organization in the face of major social and environmental challengesSee the course webpageImportant notesCourse in French : GEST 60401 It is highly recommended to take this course at the end of the program.
You must choose 3 courses including at least 2 from block 2.
Block 1
-
MATH 60607AAlgorithms for Optimization and Big Data AnalysisSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3The course is organized in 4 themes that will cover various aspects of algorithmic for big data, starting from sequential programming and ending with distributed computing. In the first part of the course, the student will learn to analyze an algorithm from the computational complexity and memory requirement. The second theme in the course deals with parallel computing with shared memory. The efficiency of the parallelization and memory safety will be discussed and analysed. In the third theme, the message passing interface (MPI) will be explored, which consists in simultaneous and collaborative parallel computing without shared memory. Finally, the basics of distributed computing, its strength and requirements will be introduced. The choice of the best approach toward the resolution of a problem will depend on the problem and the nature of the data.Themes covered
Theme 1 - Sequential programming algorithmic analysis.
Theme 2 - Parallel computing with shared memory (using threads).
Theme 3 - Synchronous parallel computing without shared memory (MPI).
Theme 4 - Distributed computing.See the course webpageImportant notesCourse in French : MATH 60607 -
MATH 60612AApplication models in Business AnalyticsSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3Topics covered in this course include optimization problems, strategic decision making, and forecasting in the context of a variety of application areas (health care, sports, energy production, environmental management, politics, transportation, scheduling, etc.) In each application context, modeling and implementation issues are discussed, along with the relevant solution methodologies. Discussed applications are based on real cases published in scientific journals.Themes covered
Health care (assignment scheduling localization collection and distribution diagnostics planning)
Sports (performance optimization performance analysis risk and reliability analysis organization of sport events scheduling routing)
Energy (transmission distribution and use global techno-economic models optimization of large problems non-convex optimization)
Environment (multi-criteria and multi-objective optimization data envelopment analysis stochastic optimization robust optimization)
Public affairs and national defense (localization and coverage prevention and detection assignment fairness)
Humanitarian aid and crisis management (logistics networks routing transportation distribution and storage simulation heuristics decision support systems)
Transportation and distribution (routes schedules on-demand transportation learning simulation-based optimization)
Human resource management (scheduling coverage modelling of collective agreements rotating schedules heuristics and metaheuristics)
Academic management (planning scheduling graph coloring heuristics integrated systems)
Customer relationship management (big data recommendation systems segmentation revenue management bi-level optimization)
Production and supply chains (diet issues supply networks competition hierarchy reverse logistics game theory).See the course webpageImportant notesCourse in French: MATH 60612 -
MATH 60615ADecision AnalysisSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3Mathematical and numerical techniques will be illustrated using examples from a diverse range of applications of management and a number of case studies. Students will learn to model and analyze a decision problem. They will also be exposed to software that can implement these models.Themes covered
1) Probabilistic modeling
2) Monte-Carlo simulation
3) Decision trees
4) Expected utility theory
5) Multiple-criteria analysis
6) Analytical hierarchy processSee the course webpageImportant notesCourse in French : MATH 60615 -
MATH 60638AForecasting MethodsSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3Students will become familiar with the use of key techniques such as smoothing, regression, time series and neural networks. Methods for model evaluation and selection, as well as methods for estimating forecast errors, are also on the program. The R software will be used.Themes covered
1. Basic ideas good and bad practices evaluation methods
2. Basic tools in forecasting: Naive predictions; ACF PACF stationarity differentiation; Expert opinion
3. Exponential smoothing: Simple Holt Holt-Winters and state-space models; Double seasonal methods
4. Multiple regression: Durbin-Watson test; Linear regression models with ARMA errors
5. Time Series: ARIMA SARIMA and ARMAX models
6. Artificial neural networks: Structure and estimation
7. Multivariate time seriesSee the course webpageImportant notesCourse in French : MATH 60638 -
MATH 60617AImplementation of Business Optimization ModelsSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3In this course, the student will learn to: 1) recognize the type of model adapted to a management problem; 2) formulate this problem using an appropriate mathematical model; 3) implement and solve this model using a dedicated software (e.g. GAMS, the IBM ILOG CPLEX Optimization Studio software) validate, analyze and present the results obtained.Themes covered
1 : Introduction to mathematical modelling
2 : Implementation of an optimization model using a dedicated software
3 : Linear models
4 : Network models
5 : Integer linear models and linearization techniques
6: Applications (e.g. location problems distribution problems scheduling problems etc.)
7 : Optimization models with multiple objectives
8 : Stochastic programming models
9 : Dynamic programming modelsSee the course webpageImportant notesCourse in French : MATH 60617 -
MATH 60621ANatural Language ProcessingSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3The subjects covered will include the structure of natural language data and how it may be used for downstream tasks such as document search and classification, text generation, and text summarization, using both heuristic-based and neural-network based language modeling. This course expects previous experience with the programming language Python, though no previous experience with machine learning is required.Themes covered
The nature of text data
Preprocessing: tokenization and lemmatization
Bag-of-words topic models and naive classification
N-gram language models (Markov models)
Hidden Markov models and part-of-speech tagging
Distributed representations and vector semantics
Recurrent neural language models LSTMs and language generation
Transformers and masked language modeling
Encoder models and semantic search
Encoder-decoder models text summarization and translationSee the course webpageImportant notesCourse in French: MATH 60621 -
MATH 60603AStatistical LearningSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3This course introduces basic and some advanced methods in unsupervised learning (e.g. dimensionality reduction, cluster analysis) and supervised learning (e.g. parametric model, trees and random forests, boosting). Examples of applications in management illustrate the use of these methods.Themes covered
- Basics of supervised and unsupervised learning.
- Dimensionality reduction techniques (principal components and factor analysis).
- Hierarchical and non-hierarchical cluster analysis.
- Basics of predictive modeling: Sample division cross-validation bootstrap.
- Parametric models for predictive modeling.
- Association rules (market basket analysis).
- Basic recursive partitioning methods: CART random forest variable importance boosting trees.
- Other topics: Support vector machines nearest neighbor methods basic survival analysis missing data.See the course webpageImportant notesCourse in French : MATH 60603 This courses is not credited in the "Intelligence d'affaires" specialization.
Cours mutuellement exclusif(s) : MATH 60600(A) et MATH 60602 Vous ne pouvez pas vous inscrire à ce cours si vous avez postulé ou réussi un de ces cours ou si un de ces cours fait partie de votre structure: MATH 60600(A) ou MATH 60602. -
MATH 60623AStochastic Optimization and Reinforcement LearningSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3The course equips students with a diverse toolkit for tackling sequential decision-making problems under uncertainty using stochastic optimization. Students learn how to model these problems effectively and select the most appropriate solution strategies, ranging from classical optimization methods to cutting-edge techniques leveraging neural networks and reinforcement learning. The course requires a strong understanding of algorithmic thinking and computer programming.Themes covered
Derivative-based / derivative-free stochastic optimization
Markov decision processes and dynamic programming
Approximate dynamic programming
Reinforcement learning
Neural networks and deep reinforcement learning
Two-stage / Multistage stochastic programming
Policy design for sequential decision-making problemsSee the course webpageImportant notesCourse in French : MATH 60623
Block 2
-
MATH 70611AAdvanced Data AnalyticsSubjectMathématiquesProgramPhDLocationCôte-des-NeigesInstruction modeOn-site learningCredits3The theoretical concepts and ideas underlying these methods will be discussed in detail. The practical use of these methods will also be discussed using examples from several research areas in management. To this end, the language R will be the tool of choice as most recent methods appear in R.Themes covered
- Regularization and variable selection in regression models
- Advanced tree-based methods and boosting
- Prediction intervals
- Analysis of functionnal data
- Analysis of spatial dataSee the course webpageImportant notesCourse in French : MATH 70611 Equivalent course(s) : MATH 60611(A), MATH 80619(A) -
MATH 80601ABayesian ModelingSubjectMathématiquesProgramPhDInstruction modeOn-site learningCredits3Mathematical review. Basics of Markov Chain Monte Carlo methods and sampling algorithms, with a focus on off-the-shelf software (e.g., OpenBugs, Stan, INLA). Approximation methods. Hierarchical modeling, with a focus on latent Gaussian models.Themes covered
Introduction to the Bayesian paradigm
Formulation comparison and evaluation of Bayesian models
Sampling algorithms and Markov chain Monte Carlo methods
Computational strategies for inference
Hierarchical models
Advanced topicsSee the course webpageImportant notesCourse in French : MATH 80601 -
MATH 80627AComplex Networks AnalysisSubjectMathématiquesProgramPhDLocationCôte-des-NeigesInstruction modeOn-site learningCredits3Would it be from social networks (Facebook, Twitter for instance), or from their own data (such as email exchanges), organizations have access to important amounts of data. This data may be of specific nature, namely, instead of a description of each observation by characteristics, relations between observations may be known. Studying these relations involves specific techniques. The nature of the data «relations between objects » makes sampling a difficult task. For that reason, it is usually better to work on the whole dataset, which is challenging from the algorithmic point of view.See the course webpageThemes covered
Centrality analysis
Community detection
Network embeddings
Network descriptors
Distances in networks -
MATH 80680ADynamic Optimisation in ManagementSubjectMathématiquesProgramPhDLocationCôte-des-NeigesInstruction modeOn-site learningCredits3See the course webpageAt the end of the course, the students should be able to identify a business management situation presenting a dynamic optimization problem, represent this problem using a suitable mathematical model, and solve it using one of the available techniques of dynamic optimization.
-
MATH 80685AGame TheorySubjectMathématiquesProgramPhDInstruction modeOn-site learningCredits3The course covers static noncooperative and cooperative games, bargaining procedures, repeated games, differential games, and games played over event trees. The elements of each class of games are defined rigorously, the main issues are discussed, the solution concepts are introduced, and their computation challenges are highlighted. Examples from different areas of management science are provided to illustrate the concepts and see the limits of the models considered.See the course webpageThemes covered
Noncooperative games
Cooperative games
Repeated games
Bargaining procedures
Optimal control and dynamic programming (a brief introduction)
Differential games
Dynamic games played over event trees. -
MATH 80655ALarge-Scale Optimization Models and ApplicationsSubjectMathématiquesProgramPhDLocationCôte-des-NeigesInstruction modeOn-site learningCredits3Advanced modeling approaches are studied, including non-compact models with exponentially many constraints or variables, two-stage models, and bilevel models. Various solution techniques are explored, such as branch-and-bound, cutting-plane methods, branch-and-cut, column generation, and Benders decomposition. Applications span fields such as distribution management, transportation, network design, routing, logistics, production planning, energy systems, telecommunications, disaster management, last-mile delivery, and healthcare logistics. The course incorporates contemporary topics from recent scientific publications. It also includes hands-on implementation of these models, and a course research project.See the course webpageThemes covered
Introduction to modeling with integer variables; well-solved problems
Optimality relaxation bounds complexity and problem reductions
Branch-and-bound and branch-and-cut (with application to e.g. traveling salesman problems)
Column generation (with application to e.g. vehicle routing problems)
Branch-and-price (with application to e.g. location and routing problems)
Two-stage models and Benders decomposition (with application to e.g. facility location)
Linearization of bilevel models (with application to e.g. network design) -
MATH 80648AMachine Learning II: Deep LearningSubjectMathématiquesProgramPhDLocationCôte-des-NeigesInstruction modeOn-site learningCredits3This course aims to introduce the basic techniques of deep learning including feedforward neural networks, convolutional neural networks, and recurrent neural networks. We will also cover recent progress on deep generative models. Finally, we will introduce how to apply these techniques to natural language understanding and graph analysis.Themes covered
Machine Learning Basics
Feedforward Neural Networks Optimization Tricks
Convolutional Neural Networks
Recurrent Neural Networks
Deep Learning for Natural Language Understanding
Deep Learning for Analyzing graphs/networks
Deep Generative ModelsSee the course webpageImportant notesCourse in French : MATH 80648 Equivalent course(s) : MATH 60630(A) or MATH 80600(A)
Préalable(s) : MATH 60600(A) ou MATH 60629(A) ou MATH 80629(A) ou être admis au Doctorat (Ph. D.). -
MATH 80624AQuantitative Risk Management Using Robust OptimizationSubjectMathématiquesProgramPhDLocationCôte-des-NeigesInstruction modeOn-site learningCredits3More specifically, students will become familiar with the main tools that are used in the application of the robust optimization paradigm: convex theory, data-driven uncertainty sets design, adjustable decision rules, tractable reformulation, and decomposition algorithms for problems of infinite size. The course will also cover a set of practical applications where the use of such tools is called-for. Applications will be inspired from a diversified range of fields of practice.See the course webpageThemes covered
1) Why is there a recent surge of interest in robust optimization?
2) Robust counterpart of Linear Programs
3) Data-driven Uncertainty Set Design
4) Robust Nonlinear Programming
5) Adjustable Robust Linear Programming
6) Value of Flexibility Using Tractable Decision Rules
7) Globalized Robust Counterparts
8) Distributionally Robust Optimization
9) Robust Markov Decision Processes
10) Robust Preference Optimization
11) Pareto Robust Optimization
12) A Survey of Recent Applications
-
MATH 70630AResponsible AISubjectMathématiquesProgramPhDLocationCôte-des-NeigesInstruction modeOn-site learningCredits3Responsible Artificial Intelligence (RAI) is about ensuring that socioenvironmental responsibility is a fundamental and permeating consideration in all stages (conception, evaluation, deployment, monitoring, etc.) of AI development and governance. This is necessary to address and prevent harms and injustices, setting the course of AI development in a direction of sustainable benefit to our interconnected world. RAI requires a thoughtful and pragmatic synthesis of approaches from wide-ranging fields. The goal of this course is to equip students with the qualitative, quantitative, critical, reflective, and practical tools to bridge the gap between theory and practice, in order to make responsible AI a reality. The course covers an evolving set of relevant topics such as user studies, participatory design, significance testing, generalization, control trials, bias, interpretability and explainability, FAccT (fairness, accountability, transparency), equity, ethics, robustness, stakeholder consultations, data governance, digital labour, peer review, reliability engineering, information security, privacy, verification, auditing, reproducibility, red-teaming, unit-testing, sandboxing, scenario planning, risk, and impact analysis.Themes covered
Fundamental concepts theories practices in responsible AI development
Machine learning basics data cleaning and visualization generalization hypothesis testing statistical significance causality
FAccT (fairness accountability transparency)
Ethics safety and alignment robustness agency
Risk and Impact analysis reliability engineering sustainability
Governance power justice digital labour
Peer review and scientific process
User experience design human-centred computing
Open-source privacy information securitySee the course webpageImportant notesCourse in French ; MATH 70630 Equivalent course(s) : MATH 80630A Prerequisite(s) : MATH 60600(A), MATH 60603(A), MATH 60629(A) -
MATH 80618ASample Efficient OptimizationSubjectMathématiquesProgramPhDLocationCôte-des-NeigesInstruction modeOn-site learningCredits3This course presents methods that combine domain knowledge from management, science or engineering within classical ML and OR techniques to yield practice-relevant optimization tools. Topics include Gaussian process modeling, Bayesian optimization, dimensionality reduction, variance reduction, continuous and/or discrete simulation-based optimization, physics-informed optimization, surrogate modeling. Concepts are illustrated using practical applications. The course presents established and emerging research through lectures and paper discussions. It includes guest research lecturers from major technology companies and / or top academic institutions worldwide.See the course webpageThemes covered
Gaussian processes
Bayesian optimization
Dimensionality reduction
Variance reduction
Continuous simulation-based optimization
Discrete simulation-based optimization
Surrogate modeling -
MATH 80621ASurvival and Longitudinal Data AnalysisSubjectMathématiquesProgramPhDLocationCôte-des-NeigesInstruction modeOn-site learningCredits3The main characteristic of survival analysis and longitudinal studies is that subjects are followed over time, thereby allowing to study the evolution of some phenomenon of interest. This course will focus on the fundamental concepts for analyzing survival and correlated data and present statistical methods that account for the temporal dynamics, correlation and partial information they present. Throughout the course, methods will be illustrated using concrete examples drawn from several areas of management. Practical considerations will be emphasized so that the student can carry out a thorough analysis of survival and correlated data by the end of the course.See the course webpageThemes covered
1. Non-parametric survival analysis
2. Parametric models for survival analysis
3. Cox proportional hazards model and extensions
4. Recurrent events and correlated survival times
5. Linear models for longitudinal data: covariance structure and random effects
6. Generalized linear models for longitudinal data: covariance structure and random effects
7. Missing data -
MATH 80617ATopics in Pricing ModelsSubjectMathématiquesProgramPhDInstruction modeOn-site learningCredits3See the course webpageThemes covered
- The economics and marketing of pricing
- Pricing strategically in (imperfectly) competitve markets while anticipating other firms' responses
- Assessing the impact of price on demand (and other variables e.g. perception and buying intention) using easy-to-implement statistical models.
- Multi-attribute models and conjoint analysis and their use in measuring consumer's perception of price
- Adapting prices to diffusion effects (e.g. social imitation and word-of-mouth effects)
- Pricing perishable products and services
2 compulsory activities and the thesis
Before completing the 24-credit thesis, you must successfully complete 2 non-credit activities.
-
RECH 66248AResearch WorkshopSubjectRecherche, thèse, mémoireProgramMaîtrise en gestion (M. Sc.)LocationOtherInstruction modeAsynchronous distance learningCredits0It is necessary to have identified the subject and the person(s) who will supervise the dissertation before registering for this workshop. Before registering the thesis topic and submitting the study plan, it is essential to discuss and validate their content with the person(s) who will act as your research supervisor(s). The result of this research workshop (requirement met) does not count towards your grade point average.See the course webpageImportant notesCourse in French : RECH 66248 The research workshop must be successfully achieved before completing 21 program credits and before the data collection, if required for your thesis.
Préalable(s) : ETHI 60113(A) (Co) ou ETHI 66201(A) (Co) -
ETHI 66201AResponsible Conduct of Research: From Norms to PracticeSubjectÉthique et gouvernanceProgramMaîtrise en gestion (M. Sc.)LocationOtherInstruction modeAsynchronous distance learningCredits0Throughout this course, students will become familiar with what constitutes ethically and responsibly conducted research. They will be able to identify and assess potential problems and misconducts, to recognize individual and institutional responsibilities, and to mobilize appropriate mechanisms for resolution. The course will enable them to identify problematic situations in research and deploy appropriate analyses and/or procedures to prevent or manage them.Themes covered
Responsible conduct of research (RCR): History and definitions
The values underlying the responsible conduct of research
Framing the responsible conduct of research
Responsible research conduct issues related to research contexts fields or methods
Research ethics
Good research data management
Conflicts of interest in research
Power issues in research
Dissemination of research results: good practices and issues
Societal impacts of research
Misconducts in researchSee the course webpageImportant notesCourse in French : ETHI 66201 All information about this course will be presented in ZoneCours. A synchronous distance learning session will be offered each term. For more information, visit the supervised project page.
This workshop is only open to students in the Thesis stream. If you are in the Supervised Project stream, you must register to the ETHI 66200(A) workshop. -
RECH 66218AThesisSubjectRecherche, thèse, mémoireProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesOtherInstruction modeOn-site learningCredits24See the course webpageImportant notesCourse in French: RECH 66218 For more information, please check this page www.hec.ca/en/students/my-program/msc/thesis
Supervised project
Supervised project
- 12 courses (36 credits)
- Supervised project (9 credits)
6 specialization courses
-
MATH 60606AFundamentals of OptimizationSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3The Simplex algorithm and key optimization principles in Linear Programming are explored, including sensitivity analysis and duality. In Integer Linear Programming, basic concepts, logical relations and their applications are studied, along with branch-and-bound and branch-and-cut algorithms. The Non-Linear Programming section covers convexity and complementary conditions, as well as gradient- and Newton-based algorithms. Finally, foundational concepts of classical heuristics and metaheuristics are introduced. The course develops essential skills for solving complex optimization problems in various fields.Themes covered
Linear programming
The simplex algorithm
Sensitivity analysis and duality
Integer programming and its applications
Branch-and-bound and branch-and-cut algorithms
Basic notions and algorithms in non-linear programming
Convexity KKT Conditions and Optimality
Gradient- and Newton-based algorithms
Heuristics and metaheuristicsSee the course webpageImportant notesCourse in French : MATH 60606 -
MATH 60617AImplementation of Business Optimization ModelsSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3In this course, the student will learn to: 1) recognize the type of model adapted to a management problem; 2) formulate this problem using an appropriate mathematical model; 3) implement and solve this model using a dedicated software (e.g. GAMS, the IBM ILOG CPLEX Optimization Studio software) validate, analyze and present the results obtained.Themes covered
1 : Introduction to mathematical modelling
2 : Implementation of an optimization model using a dedicated software
3 : Linear models
4 : Network models
5 : Integer linear models and linearization techniques
6: Applications (e.g. location problems distribution problems scheduling problems etc.)
7 : Optimization models with multiple objectives
8 : Stochastic programming models
9 : Dynamic programming modelsSee the course webpageImportant notesCourse in French : MATH 60617 -
MATH 60629AMachine Learning I: Large-Scale Data Analysis and Decision MakingSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3Massive datasets are now common and require scalable analysis tools. Machine learning provides such tools and is widely used for modelling problems across many fields including artificial intelligence, bioinformatics, finance, marketing, education, transportation, and health. In this context, we study how standard machine learning models for supervised (classification, regression) and unsupervised learning (for example, clustering and topic modelling) can be scaled to massive datasets using modern computation techniques (for example, computer clusters). In addition, we will discuss recent models for recommender systems as well as for decision making (including multi-arm bandits and reinforcement learning). Through a course project students will have the opportunity to gain practical experience with the analysis of datasets from their field(s) of interest. A certain level of familiarity with computer programming will be expected.Themes covered
1) distributed and parallel computation
2) Supervised learning
3) Non-supervised learning
4) Reinforcment learning
5) Recommendations system
6) Sequential decision making
See the course webpageImportant notesCourse in French : MATH 60629 Prerequisite(s) : MATH 60600(A) and MATH 60602. Only for specializations other than Data Sc. and Bus. Analytics. The PhD course MATH 80629A is now a MSc course -
MATH 60621ANatural Language ProcessingSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3The subjects covered will include the structure of natural language data and how it may be used for downstream tasks such as document search and classification, text generation, and text summarization, using both heuristic-based and neural-network based language modeling. This course expects previous experience with the programming language Python, though no previous experience with machine learning is required.Themes covered
The nature of text data
Preprocessing: tokenization and lemmatization
Bag-of-words topic models and naive classification
N-gram language models (Markov models)
Hidden Markov models and part-of-speech tagging
Distributed representations and vector semantics
Recurrent neural language models LSTMs and language generation
Transformers and masked language modeling
Encoder models and semantic search
Encoder-decoder models text summarization and translationSee the course webpageImportant notesCourse in French: MATH 60621 -
MATH 60603AStatistical LearningSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3This course introduces basic and some advanced methods in unsupervised learning (e.g. dimensionality reduction, cluster analysis) and supervised learning (e.g. parametric model, trees and random forests, boosting). Examples of applications in management illustrate the use of these methods.Themes covered
- Basics of supervised and unsupervised learning.
- Dimensionality reduction techniques (principal components and factor analysis).
- Hierarchical and non-hierarchical cluster analysis.
- Basics of predictive modeling: Sample division cross-validation bootstrap.
- Parametric models for predictive modeling.
- Association rules (market basket analysis).
- Basic recursive partitioning methods: CART random forest variable importance boosting trees.
- Other topics: Support vector machines nearest neighbor methods basic survival analysis missing data.See the course webpageImportant notesCourse in French : MATH 60603 This courses is not credited in the "Intelligence d'affaires" specialization.
Cours mutuellement exclusif(s) : MATH 60600(A) et MATH 60602 Vous ne pouvez pas vous inscrire à ce cours si vous avez postulé ou réussi un de ces cours ou si un de ces cours fait partie de votre structure: MATH 60600(A) ou MATH 60602. -
MATH 60604AStatistical ModellingSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3The course will focus on regression models, including linear models, generalized linear models and models for longitudinal data with random effects and correlation structures. For each of these models, basic inference principles will be covered, notably hypothesis testing and estimation procedures.Themes covered
Basic principles in inference and statistical modeling
Linear models
Generalized linear models
Models for longitudinal data and correlated data
Introduction to survival analysisSee the course webpageImportant notesCourse in French : MATH 60604 This course is not credited in the "Intelligence d'affaires" specialization.
Cours mutuellement exclusif(s) : MATH 60619(A) Vous ne pouvez pas vous inscrire à ce cours si vous avez postulé ou réussi le cours MATH 60619(A), ou si le cours MATH 60619(A) fait partie de votre structure.
1 MSc program course
-
GEST 60401AComparative Perspectives on Societal Issues and Business OrganisationsSubjectGestionProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3The course is based on the reading and discussion of fundamental texts in social science, more particularly in economics, sociology, anthropology and history. The objective is for students to apprehend major social and environmental issues and to see business organizations from a global perspective. The course thus aims at supporting students in becoming critically minded and ethically oriented actors of society and of the business world, in order to address business ethics issues and major social and environmental issues in a more informed and reflexive way. A key aspect of the course is coteaching: two instructors - one from the Department of applied economics, one from the Department of management - coteach the course, thereby providing comparative perspectives on major social and environmental issues, business organizations, and management.Themes covered
- The emergence of Western modern society
- The model of modern Western society and its social and environmental implications
- The economic and social foundations of business organizations
- The individual and collective driving forces of business organizations
- The business organization in the face of major social and environmental challengesSee the course webpageImportant notesCourse in French : GEST 60401 It is highly recommended to take this course at the end of the program.
You can either choose:
- 5 optional courses, or
- 4 optional course and 1 elective
Optional courses
-
MATH 70611AAdvanced Data AnalyticsSubjectMathématiquesProgramPhDLocationCôte-des-NeigesInstruction modeOn-site learningCredits3The theoretical concepts and ideas underlying these methods will be discussed in detail. The practical use of these methods will also be discussed using examples from several research areas in management. To this end, the language R will be the tool of choice as most recent methods appear in R.Themes covered
- Regularization and variable selection in regression models
- Advanced tree-based methods and boosting
- Prediction intervals
- Analysis of functionnal data
- Analysis of spatial dataSee the course webpageImportant notesCourse in French : MATH 70611 Equivalent course(s) : MATH 60611(A), MATH 80619(A) -
MATH 60607AAlgorithms for Optimization and Big Data AnalysisSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3The course is organized in 4 themes that will cover various aspects of algorithmic for big data, starting from sequential programming and ending with distributed computing. In the first part of the course, the student will learn to analyze an algorithm from the computational complexity and memory requirement. The second theme in the course deals with parallel computing with shared memory. The efficiency of the parallelization and memory safety will be discussed and analysed. In the third theme, the message passing interface (MPI) will be explored, which consists in simultaneous and collaborative parallel computing without shared memory. Finally, the basics of distributed computing, its strength and requirements will be introduced. The choice of the best approach toward the resolution of a problem will depend on the problem and the nature of the data.Themes covered
Theme 1 - Sequential programming algorithmic analysis.
Theme 2 - Parallel computing with shared memory (using threads).
Theme 3 - Synchronous parallel computing without shared memory (MPI).
Theme 4 - Distributed computing.See the course webpageImportant notesCourse in French : MATH 60607 -
MATH 60612AApplication models in Business AnalyticsSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3Topics covered in this course include optimization problems, strategic decision making, and forecasting in the context of a variety of application areas (health care, sports, energy production, environmental management, politics, transportation, scheduling, etc.) In each application context, modeling and implementation issues are discussed, along with the relevant solution methodologies. Discussed applications are based on real cases published in scientific journals.Themes covered
Health care (assignment scheduling localization collection and distribution diagnostics planning)
Sports (performance optimization performance analysis risk and reliability analysis organization of sport events scheduling routing)
Energy (transmission distribution and use global techno-economic models optimization of large problems non-convex optimization)
Environment (multi-criteria and multi-objective optimization data envelopment analysis stochastic optimization robust optimization)
Public affairs and national defense (localization and coverage prevention and detection assignment fairness)
Humanitarian aid and crisis management (logistics networks routing transportation distribution and storage simulation heuristics decision support systems)
Transportation and distribution (routes schedules on-demand transportation learning simulation-based optimization)
Human resource management (scheduling coverage modelling of collective agreements rotating schedules heuristics and metaheuristics)
Academic management (planning scheduling graph coloring heuristics integrated systems)
Customer relationship management (big data recommendation systems segmentation revenue management bi-level optimization)
Production and supply chains (diet issues supply networks competition hierarchy reverse logistics game theory).See the course webpageImportant notesCourse in French: MATH 60612 -
MATH 60615ADecision AnalysisSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3Mathematical and numerical techniques will be illustrated using examples from a diverse range of applications of management and a number of case studies. Students will learn to model and analyze a decision problem. They will also be exposed to software that can implement these models.Themes covered
1) Probabilistic modeling
2) Monte-Carlo simulation
3) Decision trees
4) Expected utility theory
5) Multiple-criteria analysis
6) Analytical hierarchy processSee the course webpageImportant notesCourse in French : MATH 60615 -
MATH 60638AForecasting MethodsSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3Students will become familiar with the use of key techniques such as smoothing, regression, time series and neural networks. Methods for model evaluation and selection, as well as methods for estimating forecast errors, are also on the program. The R software will be used.Themes covered
1. Basic ideas good and bad practices evaluation methods
2. Basic tools in forecasting: Naive predictions; ACF PACF stationarity differentiation; Expert opinion
3. Exponential smoothing: Simple Holt Holt-Winters and state-space models; Double seasonal methods
4. Multiple regression: Durbin-Watson test; Linear regression models with ARMA errors
5. Time Series: ARIMA SARIMA and ARMAX models
6. Artificial neural networks: Structure and estimation
7. Multivariate time seriesSee the course webpageImportant notesCourse in French : MATH 60638 -
MATH 80648AMachine Learning II: Deep LearningSubjectMathématiquesProgramPhDLocationCôte-des-NeigesInstruction modeOn-site learningCredits3This course aims to introduce the basic techniques of deep learning including feedforward neural networks, convolutional neural networks, and recurrent neural networks. We will also cover recent progress on deep generative models. Finally, we will introduce how to apply these techniques to natural language understanding and graph analysis.Themes covered
Machine Learning Basics
Feedforward Neural Networks Optimization Tricks
Convolutional Neural Networks
Recurrent Neural Networks
Deep Learning for Natural Language Understanding
Deep Learning for Analyzing graphs/networks
Deep Generative ModelsSee the course webpageImportant notesCourse in French : MATH 80648 Equivalent course(s) : MATH 60630(A) or MATH 80600(A)
Préalable(s) : MATH 60600(A) ou MATH 60629(A) ou MATH 80629(A) ou être admis au Doctorat (Ph. D.). -
MATH 70630AResponsible AISubjectMathématiquesProgramPhDLocationCôte-des-NeigesInstruction modeOn-site learningCredits3Responsible Artificial Intelligence (RAI) is about ensuring that socioenvironmental responsibility is a fundamental and permeating consideration in all stages (conception, evaluation, deployment, monitoring, etc.) of AI development and governance. This is necessary to address and prevent harms and injustices, setting the course of AI development in a direction of sustainable benefit to our interconnected world. RAI requires a thoughtful and pragmatic synthesis of approaches from wide-ranging fields. The goal of this course is to equip students with the qualitative, quantitative, critical, reflective, and practical tools to bridge the gap between theory and practice, in order to make responsible AI a reality. The course covers an evolving set of relevant topics such as user studies, participatory design, significance testing, generalization, control trials, bias, interpretability and explainability, FAccT (fairness, accountability, transparency), equity, ethics, robustness, stakeholder consultations, data governance, digital labour, peer review, reliability engineering, information security, privacy, verification, auditing, reproducibility, red-teaming, unit-testing, sandboxing, scenario planning, risk, and impact analysis.Themes covered
Fundamental concepts theories practices in responsible AI development
Machine learning basics data cleaning and visualization generalization hypothesis testing statistical significance causality
FAccT (fairness accountability transparency)
Ethics safety and alignment robustness agency
Risk and Impact analysis reliability engineering sustainability
Governance power justice digital labour
Peer review and scientific process
User experience design human-centred computing
Open-source privacy information securitySee the course webpageImportant notesCourse in French ; MATH 70630 Equivalent course(s) : MATH 80630A Prerequisite(s) : MATH 60600(A), MATH 60603(A), MATH 60629(A) -
MATH 60623AStochastic Optimization and Reinforcement LearningSubjectMathématiquesProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesInstruction modeOn-site learningCredits3The course equips students with a diverse toolkit for tackling sequential decision-making problems under uncertainty using stochastic optimization. Students learn how to model these problems effectively and select the most appropriate solution strategies, ranging from classical optimization methods to cutting-edge techniques leveraging neural networks and reinforcement learning. The course requires a strong understanding of algorithmic thinking and computer programming.Themes covered
Derivative-based / derivative-free stochastic optimization
Markov decision processes and dynamic programming
Approximate dynamic programming
Reinforcement learning
Neural networks and deep reinforcement learning
Two-stage / Multistage stochastic programming
Policy design for sequential decision-making problemsSee the course webpageImportant notesCourse in French : MATH 60623
Elective
- Campus Abroad
- A graduate-level course at HEC Montréal or at another university
All courses at another university must be pre-approved by the academic advisor for the specialization.
1 compulsory activity and the supervised project
Before completing the 9-credit supervised project, you must successfully complete the non-credit activity.
-
ETHI 66200AResponsible Conduct of Research: From Norms to PracticeSubjectÉthique et gouvernanceProgramMaîtrise en gestion (M. Sc.)LocationOtherInstruction modeAsynchronous distance learningCredits0Throughout this course, students will become familiar with what constitutes ethically and responsibly conducted research. They will be able to identify and assess potential problems and misconducts, to recognize individual and institutional responsibilities, and to mobilize appropriate mechanisms for resolution. The course will enable them to identify problematic situations in research and deploy appropriate analyses and/or procedures to prevent or manage them.Themes covered
Responsible conduct of research (RCR): History and definitions
The values underlying the responsible conduct of research
Framing the responsible conduct of research
Responsible research conduct issues related to research contexts fields or methods
Research ethics
Good research data management
Conflicts of interest in research
Power issues in research
Dissemination of research results: good practices and issues
Societal impacts of research
Misconducts in researchSee the course webpageImportant notesCourse in French : ETHI 66200 All information about this course will be presented in ZoneCours. A synchronous distance learning session will be offered each term. For more information, visit the supervised project page.
This workshop is only open to students in the Supervised Project stream. If you are in the Thesis stream, you must register to the ETHI 66201(A) workshop. -
INDV 66202ASupervised ProjectSubjectProjet individuelProgramMaîtrise en gestion (M. Sc.)LocationCôte-des-NeigesOtherInstruction modeBlended synchronous learningOn-site learningCredits9The supervised project offers various means to showcase the knowledge they acquired. The "applied" dimension of projects constitutes the first characteristic of the supervised project stream. This can consist of an intervention mandate within an organisation (analysis work, intervention and recommendations with the use of support tools such as observation, interviews, surveys, documentary research) or a research mandate within the university (research mandate, case writing or expert opinion).Themes covered
The supervised project in one of the following forms:
A mandate to intervene in an organization (making a diagnosis; Participating in the planning and implementation of management practices; Designing tools and models that can be used as the basis for decision-making; Performance analysis of an organisation's activities; Making recommendations on a problem).
A university mandate (1) the study of a case; 2) a specific search mandate; 3) an expert opinion; 4) an entrepreneurial project).
See the course webpageImportant notesThe supervised project must last a maximum of 6 months, including report writing. To register, visit the supervised project page
Préalable(s) : ETHI 66200(A) (Co)
Deadlines for submitting admission applications
- Fall term: no later than March 1st
- Winter term: no later than September 15th
- Summer term: no admissions
International applicants
Apply in fall term to allow more time to obtain immigration documents (minimum 2 months). Please note, however, that it is possible to defer your admission to the following term free of charge if the deadlines are extended.
Conditions and required documents
Select the system in which you completed your university studies or the Accelerated Admission option if you started or completed the graduate diploma at HEC Montréal related to the master's degree.
Quebec or Canadian system
Quebec or Canadian system
Degree required
You must hold an undergraduate degree of at least 90 credits (bachelor's degree) in business administration or in a related field, or a degree deemed equivalent by the program administration.
The following fields of study are prioritized: engineering, computer science, mathematics, actuarial science, statistics, finance and quantitative science.
Minimum GPA
You must have earned GPA of at least 3.0 out of 4.3 for your undergraduate degree. If the university in which you completed this degree requires a higher GPA for admission to a graduate program, this is the average to take into account.
You have the required level of English if you meet one of the criteria showing that you are an English speaker by virtue of your education.
Otherwise, you must pass an English test or complete an English program with a level of intermediate-advanced.
Documents required for admission
You will need to provide documents as part of the admission process.
Capacity is limited for certain programs. HEC Montréal does not guarantee that all eligible applicants will be accepted.
French system
French system
Degree required
You must hold at least a general Licence degree or a State-recognized bachelor's degree after at least 3 years of university studies (180 ECTS) in management or a related field.
Not eligible:
- RNCP certifications
- Bachelor universitaire de technologie (BUT)
- Bachelor's and License degrees including a technical degree (BTS or DUT)
The following fields of study are prioritized: engineering, computer science, mathematics, actuarial science, statistics, finance and quantitative science.
Minimum GPA
You must have earned an average of at least 12 out of 20 for all years of university studies.
You have the required level of English if you meet one of the criteria showing that you are an English speaker by virtue of your education.
Otherwise, you must pass an English test or complete an English program with a level of intermediate-advanced.
Documents required for admission
You will need to provide documents as part of the admission process.
Capacity is limited for certain programs. HEC Montréal does not guarantee that all eligible applicants will be accepted.
Another system
Another system
Degree required
You must hold a State-recognized university degree that provides access to a master's program at the home university (180 ECTS) in management or a related field.
Not eligible: bachelor’s or License degrees including a technical degree (BTS, DTS, or DUT).
The following fields of study are prioritized: engineering, computer science, mathematics, actuarial science, statistics, finance and quantitative science.
Minimum GPA
You must have earned an average of at least 12 out of 20 or a comparable average for all years of university studies according to the country’s grading system.
You have the required level of English if you meet one of the criteria showing that you are an English speaker by virtue of your education.
Otherwise, you must pass an English test or complete an English program with a level of intermediate-advanced.
Documents required for admission
You will need to provide documents as part of the admission process.
Capacity is limited for certain programs. HEC Montréal does not guarantee that all eligible applicants will be accepted.
Accelerated Admission
Accelerated Admission from the Graduate Diploma in Management – Data Science and Business Analytics
- You must have completed 24 credits from a Graduate Diploma program with a GPA of at least 2.7 out of 4.3.
- You must complete the Short Administrative Request form.
Admission is not automatic.
Prior learning in the short program will be recognized based on the structure of the Graduate Diploma in place when they applied.
Estimated tuition fees
The amounts below are approximate. For the detailed amounts per credit or per term, see the tuition fee schedule for the thesis stream (PDF, 109 Kb) or the supervised project stream (PDF, 105 Kb). These amounts do not include the cost of health insurance, course materials, housing, or other expenses.
Each term, you will receive a bill with the exact amount you owe based on your credit load.
Fees are calculated per term for the thesis stream and per credit for the supervised project stream.
You pay the Quebec rate if you are a resident of Quebec according to certain criteria, such as having a Quebec birth certificate or a Québec Selection Certificate.
Total cost of a 45-credit program at full time
- Thesis (4 terms): $6,900
- Supervised project: $7,600
See if you meet one of the Quebec residence criteria.
You pay the Canadian rate if you are a citizen by birth or naturalization, an Indigenous person, or a permanent resident of Canada.
Total cost of a 45-credit program at full time
- Thesis (4 terms): $16,500
- Supervised project in English: $22,000
- Supervised project in French: $17,200
See whether you can receive an exemption and pay the Quebec rate.
Through an agreement between governments, you are eligible for an exemption allowing you to pay the Quebec rate instead of the international rate.
Total cost of a 45-credit program at full time
- Thesis (4 terms): $6,900
- Supervised project: $7,600
Check the conditions you must meet to receive this exemption.
You pay the international rate if you are from a country outside Canada. There is no exemption for your situation.
Total cost of a 45-credit program at full time
- Thesis (4 terms): $33,500
- Supervised project: $37,800
These fees give you priority registration for the course INTE 60004 Expérience Québec (in French) and a French language course to help you adapt to the Quebec and Canadian job markets.
See whether you can receive an exemption and pay the Quebec or Canadian rate.
To ensure the proper rate is applied, you may need to provide documents proving your legal status once you have been admitted.
Scholarships
Every year, HEC Montréal awards close to $1.6 million in scholarships and other forms of awards to M.Sc. students. What a great way to help finance your studies!
These scholarships, worth from $2,000 to $10,000, are awarded by the MSc in Administration program office to the top candidates admitted, based on their academic record at the time of admission.
There are no applications to be completed; successful candidates will be notified by e-mail.
Federal and Quebec government granting agencies award scholarships worth $20,000 and $27,000 to students with an excellent average and who wish to pursue their studies at the Master’s level.
Social Sciences and Humanities Research Council of Canada (SSHRC)
Annual $27,000 scholarships for students in all specializations except Financial Engineering, Data science and business analytics and Business Intelligence.
Deadline: December 1st every year
Natural Sciences and Engineering Research Council of Canada (NSERC)
Annual $27,000 scholarships for students in the Financial Engineering, Data science and business analytics and Business Intelligence specializations.
Deadline: December 1st every year
Fonds de recherche du Québec – Société et culture (FRQSC)
Annual $20,000 scholarships, awarded for two years, for students in all specializations except Financial Engineering, Data science and business analytics and Business Intelligence.
Deadline: Usually during the 2nd week of October
Fonds de recherche du Québec – Nature et technologie (FRQNT)
Annual $20,000 scholarships, awarded for two years, for students in the Financial Engineering, Data science and business analytics and Business Intelligence specializations.
Deadline: Usually during the 1st week of October
$15,000 scholarships awarded for a 4-6 months research internship in a company. Students in the supervised project or thesis streams are eligible for these scholarships.
Applications may be submitted at any time (ideally 3 months before the project begins)
Exemptions offered by HEC Montréal
HEC Montréal offers a number of differential tuition fee exemptions to international newly admitted in the thesis stream.
Agreement between the government of Québec and some forty countries
The Quebec government has agreements with some forty countries and a number of organizations, exempting students who come to study in the province from differential tuition fees. The quota depends on the student’s country of origin. Students must contact the persons responsible for managing this program in their home country.
For further information, see the page on exemptions from the diffrential tuition fees.
- Financial assistance from the Government of Quebec
- Work-study programs
- Research and teaching assistantships (RAs and TAs)