* Please note that the former title of this program was: Master of Science in Systems Science.
Summary
- Degree offered: Master of Science (MSc)sRegistration status options: Full-time; Part-time
- Language of instruction:
- English
- Program option (expected duration of the program):
- within two years of full-time study
- Academic units: Faculty of Engineering, Telfer School of Management, Department of Mathematics and Statistics, Department of Economics.
Program Description
The Systems Science and Engineering program provides qualified students with the opportunity for master's-level study in a broad range of areas that emphasize transdisciplinary work in the context of general systems analysis. The emphasis in Systems Science and Engineering is on the development of analytical and integration skills for use in the resolution of complex applied problems that require a broad-based perspective.
Many professors in Information Technology and Engineering, Mathematics and Statistics, Administration, Economics, and other disciplines are active in the Systems Science and Engineering program as instructors, student advisers and thesis directors. Others are interested in ongoing Systems Science and Engineering activities including the seminar series, and Systems Science and Engineering applications days.
The graduate program in System Science is an interdisciplinary program specially designed for those who are interested in the analysis and modelling (mathematical and computer) of natural and man-made systems. It provides the professional with skills and knowledge required to understand, control, predict and optimize behaviour in a variety of fields from engineering and computer science to management and applied economics. The program is supervised by a Committee composed of representatives from the Department of Economics, the School of Information Technology and Engineering, the Telfer School of Management, and the Department of Mathematics and Statistics.
To accommodate part-time students, the core courses are usually offered in the late afternoon or evening.
Main Areas of Research
Their areas of research, both theoretical and applied, span a wide variety of fields:
- Operations research
- Deterministic and probabilistic modelling
- Optimization
- Computer science
- Information systems
- Control
- Economic modelling
Other Programs Offered Within the Same Discipline or in a Related Area
- Graduate Diploma Systems Science and Engineering
- Master of Systems Science and Engineering (MSysScEng)
Fees and Funding
- Program fees:
The estimated amount for university fees associated with this program are available under the section Finance your studies.
International students enrolled in a French-language program of study may be eligible for a differential tuition fee exemption.
- To learn about possibilities for financing your graduate studies, consult the Awards and financial support section.
Notes
- Programs are governed by the academic regulations in effect for graduate studies.
- In accordance with the University of Ottawa regulation, students have the right to complete their assignments, examinations, research papers, and theses in French or in English. Research activities can be conducted either in English, French or both, depending on the language used by the professor and the members of his or her research group.
Graduate Studies Office, Faculty of Engineering
STE 1024
800 King Edward Ave.
Ottawa ON Canada
K1N 6N5
Tel.: 613-562-5347
Fax.: 613-562-5129
Email: engineering.grad@uottawa.ca
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For the most accurate and up to date information on application deadlines, language tests and other admission requirements, please visit the specific requirements webpage.
To be eligible, candidates must:
- Have a bachelor’s degree in Computer Science, Economics, Engineering, Mathematics, Operations Research, Science or a related area with a minimum average of B (70%).
Note: International candidates must check the admission equivalencies for the diploma they received in their country of origin.
- Undergraduate courses in probability, linear algebra, differential equations and computer programming are prerequisites for the core courses of the Program. Details regarding the level and content of prerequisite courses are included in the information package which is sent to all applicants. If a student lacks any of these courses, he will normally be required to complete them as a condition of admission. Entering students who lack the required undergraduate preparation may be permitted to enter a qualifying program.
- Identify at least one professor who is willing to supervise your research and thesis. We recommend that you contact potential thesis supervisors as soon as possible.
Language Requirements
Applicants must be able to understand and fluently speak the language of instruction (French or English) in the program to which they are applying. Proof of linguistic proficiency may be required.
Applicants whose first language is neither French nor English must provide proof of proficiency in the language of instruction.
Note: Candidates are responsible for any fees associated with the language tests.
Notes
- The admission requirements listed above are minimum requirements and do not guarantee admission to the program.
- Admissions are governed by the academic regulations in effect for graduate studies.
Requirements for this program have been modified. Please consult the 2023-2024 calendar for the previous requirements.
Master’s with Thesis
Students must meet the following requirements:
| Code | Title | Units |
|---|---|---|
| Compulsory Courses: | ||
| 3 course units from: | 3 Units | |
| Systems Integration | ||
| Essential Concepts in Data Science | ||
| 12 course units from: | 12 Units | |
| Systems Engineering | ||
| Foundation of Modelling and Simulation | ||
| Applied Probability | ||
| Systems Optimization and Management | ||
| Economic System Design | ||
| Systems Integration | ||
| Essential Concepts in Data Science | ||
| Mathematics for Artificial Intelligence | ||
| Foundations and Applications of Machine Learning | ||
| Optional Course : | ||
| 3 optional course units from the list of optional courses 1 | 3 Units | |
| Thesis Proposal: | ||
| SYS 7990 | Master Thesis Proposal 2 | |
| Thesis: 3 | ||
| THM 7999 | Master's Thesis | |
List of Optional Courses
| Code | Title | Units |
|---|---|---|
| ADM 6260 | Project Management I | 1.5 Units |
| ADM 6261 | Project Management II | 1.5 Units |
| CSI 5122 | Software Usability | 3 Units |
| DTI 5175 | Mobile Commerce Technologies | 3 Units |
| DTI 5380 | Systems and Architectures for Electronic Commerce | 3 Units |
| DTI 6130 | Web Services | 1.5 Units |
| DTI 6160 | Cyber Security Systems and Strategies | 3 Units |
| DTI 6230 | Business Process Management and Performance Measurement | 3 Units |
| ECO 6143 | Economics of Natural Resources | 3 Units |
| ELG 5103 | Optical Communications Systems | 3 Units |
| ELG 5119 | Stochastic Processes | 3 Units |
| ELG 5170 | Information Theory | 3 Units |
| ELG 5375 | Digital Communications | 3 Units |
| ELG 5376 | Digital Signal Processing | 3 Units |
| ELG 5378 | Image Processing and Image Communications | 3 Units |
| EMP 5116 | Issues in Management and Operation of Communication Networks | 3 Units |
| EMP 5120 | Product Development and Management | 3 Units |
| GNG 5100 | Introduction to Engineering Management | 3 Units |
| GNG 5120 | Technology entrepreneurship for Engineers and Computer Scientists | 3 Units |
| GNG 5121 | Taguchi methods for efficient Engineering RD | 3 Units |
| GNG 5123 | Enterprise Architecture | 3 Units |
| GNG 5124 | Internet Technologies and Mobile Commerce | 3 Units |
| GNG 5125 | Data Science Applications | 3 Units |
| GNG 5130 | Communication and Influence for Engineers | 3 Units |
| GNG 5131 | Sales and Influence for Engineers | 3 Units |
| GNG 5140 | Engineering Design | 3 Units |
| GNG 5141 | Creativity and Innovation | 3 Units |
| GNG 5231 | Sales Engineer Internship Project | 6 Units |
| GNG 5300 | Topics in Engineering | 3 Units |
| GNG 5301 | Professional Skills and Responsibility | 3 Units |
| GNG 5310 | Topics in Industry Practice | 3 Units |
| GNG 5902 | Industry Internship Project | 6 Units |
| IAI 5101 | Foundations of Machine Learning for Scientists and Engineers | 3 Units |
| IAI 5130 | Ethics for Design, AI and Robotics | 3 Units |
| MCG 5169 | Advanced Topics in Reliability Engineering | 3 Units |
| SYS 5111 | Foundations and Applications of Machine Learning | 3 Units |
| SYS 5122 | Essential Concepts in Data Science | 3 Units |
| SYS 5295 | Ethics for Design, AI, and Robotics | 3 Units |
Note(s)
- 1
Consult the department for the list of elective courses and for the regulations governing the selection of these courses.
- 2
Candidates enrolled for the MSc degree must submit to the program committee, by the middle of their third term of enrollment in the MSc program, a clearly defined research proposal that has been approved by their thesis director. Approval of the proposal must normally be obtained by the end of the term. A student must enroll in the Master's Thesis (THM 7999) in the term immediately following the approval of the proposal. A student whose proposal is not approved on the first attempt may be permitted to submit a second proposal. Failure to obtain approval following the second submission will lead to withdrawal from the MSc program. Students required to withdraw from the MSc but who have successfully completed all the core courses are eligible to receive the graduate diploma.
- 3
Students are responsible for ensuring they have met all of the thesis requirements. Upon submission, the completed thesis will be examined by a committee of at least two professors.
Research at the University of Ottawa
Located in the heart of Canada’s capital, a few steps away from Parliament Hill, the University of Ottawa ranks among Canada’s top 10 research universities. Our research is founded on excellence, relevance and impact and is conducted in a spirit of equity, diversity and inclusion.
Our research community thrives in four strategic areas:
- Creating a sustainable environment
- Advancing just societies
- Shaping the digital world
- Enabling lifelong health and wellness
From advancing healthcare solutions to tackling global challenges like climate change, the University of Ottawa’s researchers are at the forefront of innovation, making significant contributions to society and beyond.
Research at the Faculty of Engineering
Areas of research:
- Chemical and Biological Engineering
- Civil Engineering
- Electrical Engineering and Computer Science
- Mechanical Engineering
For more information, refer to the list of faculty members and their research fields on Uniweb.
IMPORTANT: Candidates and students looking for professors to supervise their thesis or research project can also consult the website of the faculty or department of their program of choice. Uniweb does not list all professors authorized to supervise research projects at the University of Ottawa.
SYS 5100 Systems Engineering (3 units)
Controllability and observability, Euler-Lagrange equations, Pontryagin maximum principle, dynamic programming, linear quadratic regulator problem, matrix Ricatti differential equations and properties of their solution, design of optimal regulator based on steady state solution of the Ricatti differential equation, time optimal control, LaSalle bang-bang principle, applications to motor speed control, satellite attitude control, etc.
Course Component: Laboratory, Lecture, Tutorial
The following courses are recommended as prerequisites: CSI 1100, MAT 2341, (MAT 2324 or MAT 2331), MAT 2371, MAT 2375.
SYS 5110 Foundation of Modelling and Simulation (3 units)
Fundamental aspects of systems modelling and the simulation process. Elements of continuous system simulation. Issues relating to the numerical solution of ordinary differential equations. Elements of discrete event simulation Generation of random numbers and variates. Simulation validation and quality assurance. Introduction to simulation languages.
Course Component: Lecture
The following courses are recommended as prerequisites: CSI 1100, MAT 2341, (MAT 2324 or MAT 2331), MAT 2371, MAT 2375.
SYS 5111 Foundations and Applications of Machine Learning (3 units)
The capabilities and limitations of machine learning; problem formulation; supervised and unsupervised learning techniques; deploying, monitoring, and evaluating machine learning models; storytelling and assessing the results of learning; current advances in application areas such as business, law, arts, social sciences and education.
Course Component: Lecture
The courses CSI 4145, CSI 5155, ELG 5255, IAI 5100, SYS 5111 cannot be combined for units.
SYS 5120 Applied Probability (3 units)
An introduction to stochastic processes, with emphasis on regenerative phenomena. Review of limit theorems and conditioning. The Poisson process. Renewal theory and limit theorems for regenerative processes; Discrete-time and continuous-time Markov processes with countable state space. Applications to queueing.
Course Component: Lecture
The following courses are recommended as prerequisites: CSI 1100, MAT 2341, (MAT 2324 or MAT 2331), MAT 2371, MAT 2375.
SYS 5122 Essential Concepts in Data Science (3 units)
An introduction to the foundations of data science using a case study approach; overview of the data science process: types of tasks and models, data manipulation, exploratory data analysis, data summarization and data visualization; predictive modeling, descriptive modeling; reporting and deployment.
Course Component: Lecture
The courses CSI 4142, DTI 5125, DTI 5126, MAT 4373, SYS 5122 cannot be combined for units.
SYS 5130 Systems Optimization and Management (3 units)
Analysis of user requirements and model design. Data mining. Use of optimization software. Systems thinking and its application to economic systems and hierarchical systems. Applications to economic systems simulation, modeling, optimization and management.
Course Component: Lecture
The following courses are recommended as prerequisites: CSI 1100, MAT 2341, (MAT 2324 or MAT 2331), MAT 2371, MAT 2375.
SYS 5140 Economic System Design (3 units)
Introduction to the epistemology of systems thinking and its application to economic systems. Basic concepts of complex systems thinking including hierarchical systems and economic systems simulation and behaviour. Soft systems thinking. Examples from other fields of application will be reviewed from an interdisciplinary perspective.
Course Component: Lecture
The following courses are recommended as prerequisites: CSI 1100, MAT 2341, (MAT 2324 or MAT 2331), MAT 2371, MAT 2375.
SYS 5160 Systems Integration (3 units)
Planning, design of complex systems from continuous to discrete time. Synthesis of systems methodology. State estimation. Parameters identification. Discretization and stochastic effects. Dynamic, logic control. Modelling, discrete event, simulation examples.
Course Component: Lecture
Prerequisites: 6 course units from SYS 5100, SYS 5110, SYS 5120, SYS 5130, SYS 5140.
SYS 5170 Essential Concepts in Data Science (3 units)
An introduction to the foundations of data science using a case study approach; overview of the data science process: types of tasks and models, data manipulation, exploratory data analysis, data summarization and data visualization; predictive modeling, descriptive modeling; reporting and deployment.
Course Component: Lecture
The courses CSI 4142, DTI 5125, DTI 5126, MAT 4373, IAI 5120 and SYS 5170 cannot be combined for units.
SYS 5180 Mathematics for Artificial Intelligence (3 units)
Mathematical foundations (algebra, statistics) of modern artificial intelligence applicable to machine learning, deep learning, vision, natural language and speech processing. Eigenvectors and Eigenvalues. Single Value Decomposition. Principal Component Analysis. Vector/Matrix Calculus. Gradient Algorithms. Common Distributions. Maximum Likelihood Estimation. Entropy and Cross Entropy. Kullback Leibler Divergence. Viterbi Algorithm.
Course Component: Lecture
SYS 5185 Foundations and Applications of Machine Learning (3 units)
The capabilities and limitations of machine learning; problem formulation; supervised and unsupervised learning techniques; deploying, monitoring, and evaluating machine learning models; storytelling and assessing the results of learning; current advances in application areas such as business, law, arts, social sciences and education.
Course Component: Lecture
The courses CSI 5155, ELG 5255, IAI 5100, IAI 5101, MIA 5100 and SYS 5185 cannot be combined for units.
SYS 5190 Directed Readings in Systems Science (3 units)
Directed Readings in Systems Science
Course Component: Research
SYS 5295 Ethics for Design, AI, and Robotics (3 units)
The interplay between Artificial Intelligence, society, the law, and ethics; the course will explore how advances in Artificial Intelligence affect the law and other social institutions, and, conversely, how societal, legal, and ethical considerations affect the development and deployment of Artificial Intelligence technologies.
Course Component: Lecture
The courses CSI 5195, DTI 5310, ELG 5295, IAI 5130 and SYS 5295 cannot be combined for units
SYS 5975 Projet en science des systèmes / Project in Systems Science (6 crédits / 6 units)
Volet / Course Component: Recherche / Research
Les cours SYS 5190, SYS 5975 ne peuvent être combinés pour l'obtention de crédits. / Courses SYS 5190, SYS 5975 cannot be combined for units.
SYS 7990 Proposition de thèse de maîtrise / Master Thesis Proposal
Volet / Course Component: Recherche / Research