Short Graduate Program in Data Science (Doctoral Level)

Continue your specialization in data science with doctoral-level training that includes interactions with highly qualified research teams.

Overview

Bridging research and practice

Acquire or advance methodological skills or advanced specialization applicable in organizational or research settings.

Seminars providing opportunities for expert-level interaction between researchers and individuals with solid experience gained in organizational settings. These interactions can be instrumental in identifying applications and collaborations.

Extensive range of courses, including:

Advanced methods in data mining
Complex network analysis
Deep learning
Bayesian modeling
Survival and longitudinal data analysis
Responsible AI

Program characteristics

Doctoral
9 credits

Part-time

On-site

Côte-des-Neiges

Leading-edge research in this field

Faculty members associated with this doctoral specialization have published hundreds of articles in leading journals.

Several hold chairs or research professorships, including two Canada CIFAR AI Chairs.

All of them work closely with several research groups and transfer hubs, including:

Centre de recherches mathématiques (CRM)
Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT)
Group for Research in Decision Analysis (GERAD)
IVADO – artificial intelligence research consortium
MILA, artificial intelligence research centre

This program is made for you

You are currently employed and considering an advanced specialization in data science. This short graduate program can benefit individuals who already hold a master's degree in data science or a related field, such as statistics, computer science, business intelligence, business analytics, operations research, or engineering.
You have just completed a master's degree and want to continue your specialization without enrolling in a full doctoral program.
You are considering pursuing a PhD in data science, but you want to experience doctoral-level courses before making a decision.
You want to broaden your expertise after earning a PhD in a related discipline (e.g., mathematics, physics, operational research or engineering).

Other programs to strengthen your expertise and expand your network

Explore other short graduate programs (doctoral level)

Discover our other programs

Already started your program? Check your course list in HEC en ligne > Academic Progress

3 specialization seminars (9 credits)

You must choose 3 seminars from the following:

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

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

This program is offered part time. See our article (in French) to learn about the immigration rules.

Conditions and required documents

Admission requirements

Degree required

You must hold a master’s degree in a field related to the desired program or a degree deemed equivalent by the program administration.

Minimum GPA

You must have earned GPA of at least 3.0 out of 4.3.

If the university in which you completed the master's degree requires a higher GPA for admission to a doctoral program, this is the average to take into account.

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.

Estimated tuition fees

The amounts below are approximate. These amounts do not include the cost of health insurance, course materials, housing,

Each term, you will receive a bill with the exact amount you owe based on your credit load.

Residents of Quebec

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 9-credit program: $1,700

See if you meet one of the Quebec residence criteria.