PhD in administration – Data Science

Are you planning on a career in academia or business in the field of data science? Join a community of professors and researchers with valuable, recognized expertise.

Overview

Your PhD in short

Offered by HEC Montréal jointly with Concordia and McGill universities and the Université du Québec à Montréal (UQAM). This partnership gives you access to resources (faculty and courses) rarely available elsewhere in the world.

Full-time program allowing you to complete your studies in 4 or 5 years.

Tuition fees waived and competitive funding for the first four years of your studies.

“HEC helped me build my professional skills and paved the way for me to a great academic job at the American University of Beirut (AUB), the second top university in the Middle East.”

–

Imad Bou Hamad, PhD. Assistant Professor, Olayan School of Business

Program characteristics

Doctoral
90 credits

Full-time

On-site

Côte-des-Neiges

For a fascinating career

“Whereas many PhD programs aim exclusively at academic careers, HEC’s approach satisfies both academic interests and professional education.”

–

Walid Mathlouthi, PhD. Senior Data Scientist

“The teaching and support I received during my PhD studies in Management Sciences at HEC Montréal were of such high quality that I couldn’t help but succeed.”

–

Ahlem Hajjem, PhD. Professor at ESG UQAM

“The PhD program allowed me to build solid knowledge of data science methods that enables me to solve complex problems with rigor and success, and also to contribute to innovation in the industry.”

–

Marie-Hélène Roy, PhD. Lead Data Scientist – Age of Learning, California.

Among the best

The professors associated with this doctoral specialization have authored over 400 scientific papers in the top journals, including:

Bioinformatics
Biometrika
Journal of the American Statistical Association
Statistical Methods in Medical Research

Be part of research innovations

IVADO

HEC Montréal is a founding member of IVADO (research consortium in artificial intelligence). The Institute brings together 900 scientists interested in optimization (operational research) and data science.

The group has received major funding ($93.6 million) for research into big data mining.

Tech3lab

HEC Montréal is home to Tech3Lab, the largest user-experience (UX) laboratory in North America. It’s a unique milieu for advanced research in human-machine interaction and the user experience

MILA

Many of the professors in this specialization are members of MILA, Montréal’s world-renowned centre for artificial intelligence research.

Varied research interests

Methodology

Artificial intelligence
Bayesian statistics
Big data mining
Data mining
Deep learning
Genomics
Machine learning
Non-parametric methods
Recommendation systems
Statistics

Applications

Analytics of urban environments
Finance
Marketing
Network analysis
Traffic safety
User experience (UX)

An exceptional research milieu

The eleven professors mainly associated with the doctoral specialization in Data Science have substantial research funds at their disposal to assist students.

Eight of them hold chairs or professorships.

FRQ-IVADO Chair in Data Science: Professor Aurélie Labbe
Canada CIFAR Chair in Artificial Intelligence: Professor Laurent Charlin
Canada CIFAR Chair in Artificial Intelligence: Professor Jian Tang
Research Professorship in Complex Networks : Professor Gilles Caporossi
Research Professorship in Data Science: Professor Denis Larocque
Research Professorship in Statistics: Professor Debbie J. Dupuis
Professorship in Pedagogical Innovation in Active Learning of Statistics in Management: Professor Chantal Labbé
Professorship in Pedagogical Innovation in the Gamification of Learning for Data Science: professor Jean-François Plante

Marc Fredette is principal collaborator at the User Experience Chair.

Researchers in this specialization work closely with several research groups and knowledge transfer hubs, including:

In addition, HEC Montréal is an institutional member of the Canadian Statistical Sciences Institute (CANSSI).

The doctoral program in administration requires a minimum of 90 credits, structured across three phases: preparatory coursework, specialization, and the dissertation.

The program typically spans between three and six years.

Phase I – Preparation (if necessary, maximum 1 year)

The preparatory phase courses allow you to acquire the necessary knowledge before starting the PhD-level courses. These are generally graduate-level courses and do not count toward the program’s total of 90 credits.

Phase II – Specialization (maximum 2 years)

At least 7 seminars among those proposed, respecting the minimum required for each type
Uncredited workshop Responsible Conduct of Research: From Norms to Practice
Writing of scientific paper
Comprehensive exam

Minimum 5 specialization seminars

MATH 70611A

Advanced Data Analytics

Subject

Mathématiques

Program

PhD

Location

Côte-des-Neiges

Instruction mode

On-site learning

Credits

3

The 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 data

Important notes

Course in French : MATH 70611 Equivalent course(s) : MATH 60611(A), MATH 80619(A)

See the course webpage
MATH 80601A

Bayesian Modeling

Subject

Mathématiques

Program

PhD

Instruction mode

On-site learning

Credits

3

Mathematical 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 topics

Important notes

Course in French : MATH 80601

See the course webpage
MATH 80627A

Complex Networks Analysis

Subject

Mathématiques

Program

PhD

Location

Côte-des-Neiges

Instruction mode

On-site learning

Credits

3

Would 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.

Themes covered

Centrality analysis
Community detection
Network embeddings
Network descriptors
Distances in networks

See the course webpage
MATH 80648A

Machine Learning II: Deep Learning

Subject

Mathématiques

Program

PhD

Location

Côte-des-Neiges

Instruction mode

On-site learning

Credits

3

This 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 Models

Important notes

Course 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.).

See the course webpage
MATH 70630A

Responsible AI

Subject

Mathématiques

Program

PhD

Location

Côte-des-Neiges

Instruction mode

On-site learning

Credits

3

Responsible 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 security

Important notes

Course in French ; MATH 70630 Equivalent course(s) : MATH 80630A Prerequisite(s) : MATH 60600(A), MATH 60603(A), MATH 60629(A)

See the course webpage
MATH 80636A

Special Topics in Data Science

Subject

Mathématiques

Program

PhD

Instruction mode

On-site learning

Credits

3

This course covers advanced topics in Data Science. This course is typically taught by a team of professors.

See the course webpage
MATH 80621A

Survival and Longitudinal Data Analysis

Subject

Mathématiques

Program

PhD

Location

Côte-des-Neiges

Instruction mode

On-site learning

Credits

3

The 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.

Themes 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

See the course webpage

You may also choose any other equivalent seminar offered by the joint program or by a doctoral program at a Montreal university.

1 pedagogy seminar

MNGT 80470A

Teaching in Business Education

Subject

Management

Program

PhD

Location

Côte-des-Neiges

Instruction mode

On-site learning

Credits

3

Themes covered

- Teaching conceptions and learning models
- Current trends in business education
- Teaching methods
- Course planning and preparation
- Technology and teaching
- Self-assessment of the SMT
- Pedagogical innovation in business education
- Developing oneself as a teacher

Important notes

Course in French : MNGT 80470

See the course webpage

You may also choose any other equivalent course offered in the joint program.

Maximum 1 elective seminar

Phase III – Dissertation (maximum 3 years)

Writing of the dissertation
Public presentation of the dissertation proposal
Defence

Deadlines for submitting admission applications

Fall term: no later than January 5th
Winter term: no admissions
Summer term: no admissions

Currently studying in a master's program?

You can apply for admission to a doctoral program, even if you have not yet completed your current program (MSc or MBA). If your application is accepted, your admission will be conditional upon you receiving your degree before the start of the doctoral program, meaning before late August.

Conditions and required documents

Admission requirements

Degree required

You must hold one of the following degrees, or a degree deemed equivalent by the program administration:

Master of Science (MSc)
Master of Business Administration (MBA)
Graduate degree in a related field

Minimum GPA

You must have earned an average that the School deems excellent for all years of university studies.

Admission test

The admission test (GRE, TAGE MAGE or GMAT) is optional but strongly recommended to support your application.

Language requirements

Documents required for admission

You will need to provide documents as part of the admission process.

Check the list of required documents

Capacity is limited for certain programs. HEC Montréal does not guarantee that all eligible applicants will be accepted.

Program funding

At least $30,000 per year for your first 4 years of study.

This amount may include various sources of funding: bursaries, remuneration for teaching activities, research activities, etc.

A letter detailing the funding offer will be sent when you are admitted.

Financial support from the Office of Research and Knowledge Transfer

Maximum of $5,000 for the duration of studies

Depending on available funding, the Office of Research and Knowledge Transfer may grant assistance of up to $5,000 for the duration of your studies. The aim is to support your efforts in scientific research and publication, and to help you in your job search. Learn more about funded activities.

Estimated annual fees for the first 4 years of studies

For your first 4 years of studies, you will receive a tuition waver. During this time, you must pay institutional fees and medical insurance fees as applicable.

Canadian students
$0 for tuition fees $1,700 for mandatory institutional fees
Approximately $1,700 per year

Canadian students

$0 for tuition fees

$1,700 for mandatory institutional fees

Approximately $1,700 per year

International students
$0 for tuition fees $1,700 for mandatory institutional fees $700 for health insurance
Approximately $2,400 per year

International students

$0 for tuition fees

$1,700 for mandatory institutional fees

$700 for health insurance

Approximately $2,400 per year

Estimated annual fees starting the 5th year of studies

Canadian students
$2,700 for tuition fees $1,700 for mandatory institutional fees
Approximately $4,400 per year

Canadian students

$2,700 for tuition fees

$1,700 for mandatory institutional fees

Approximately $4,400 per year

French or French Belgian student
$2,700 for tuition fees $1,700 for mandatory institutional fees $700 for health insurance
Approximately $5,100 per year

French or French Belgian student

$2,700 for tuition fees

$1,700 for mandatory institutional fees

$700 for health insurance

Approximately $5,100 per year

International students
$3,300 for tuition fees $1,700 for mandatory institutional fees $700 for health insurance
Approximately $5,700 per year

International students

$3,300 for tuition fees

$1,700 for mandatory institutional fees

$700 for health insurance

Approximately $5,700 per year

Other financial resources

HEC Montréal encourages you to apply for bursaries with Quebec and Canadian funding agencies (annual competitions). Student Services (SAE) offers support for preparing your applications.

For any questions, email analyste.bourses [at] hec.ca (analyste[dot]bourses[at]hec[dot]ca).

Admission

Apply for admission

Information

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