Data Analytics involves the algorithmic processing of data for purposes of description (what happened), prediction (what will happen), or prescription (what should be done). Machine Learning focuses on the computational and statistical methods for learning patterns and associations and obtaining insights from data.
Data Analytics and Machine Learning collectively represent the forefront of technology innovation powering a wide range of applications including personalized e-commerce, cybersecurity, intelligent logistics and scheduling, financial investing, digital marketing, adaptive user interfaces, and health applications including medical imaging analysis.
University of Toronto graduates who have earned the Emphasis in Data Analytics and Machine Learning are among the most sought out engineers with AI, Data Science, and Information Engineering skills in the local and the North American job market. Some of the most common first roles taken by the graduates of this Emphasis are: data engineer, machine learning engineer, data scientist, R&D consultant, business intelligence analyst, DevOps/MLOps engineer, and senior data analyst.
Graduate students at the University of Toronto students registered in the MEng program offered in CHE, CIVMIN, ECE, MIE, MSE have access to nearly 50 graduate courses in different areas of artificial intelligence, machine learning, data science, and their applications in different disciplines of engineering, developed and taught by our outstanding engineering faculty, many of whom have earned accolades for pioneering research and exceptional teaching.
MEng Students[1] can earn[2] the Emphasis in Data Analytics and Machine Learning by successfully completing the single prerequisite course (APS 1070)[3], one core course, and three additional elective courses from the lists presented below.
Note: The Emphasis in Analytics has now been renamed to Data Analytics and Machine Learning, and will take effect starting Fall 2024.
New elective courses have been added and the option to complete a MEng project related to data analytics and machine learning may be completed in lieu of the three elective courses. Check the corresponding department website for more information on their currently available MEng projects.
[1] Master of Engineering (MEng) students in graduate units in the departments of Chemical Engineering & Applied Chemistry, Civil Engineering, Electrical & Computer Engineering, Materials Science & Engineering, and Mechanical & Industrial Engineering
[2] Some courses may satisfy the requirements of multiple emphases. Students may double-count a maximum of one course towards the requirements of any two emphases. Students cannot earn more than two emphases.
[3] Students must have completed the prerequisite course APS1070 before taking any of the core courses. However, students can take APS1070 along with their elective courses in the same term.
*View what courses MEng students who have completed the emphasis have taken. These are only sample pathways, you are welcome to create your own course pathway (that meets requirements of the emphasis) that suits your own needs.
Prerequisite Course
- APS 1070H Foundations of Data Analytics and Machine Learning (cannot be used towards the ELITE emphasis)
Summer Course Offering:
May 1 - July 17, Lec: Wednesdays 6-9pm, room TBD, Tut: 4-6pm, Thursdays, 4-6pm, room TBD
Core Courses
Completing a minimum of one core course is required from the Emphasis in Data Analytics and Machine Learning.
- MIE 1626H Data Science Methods and Statistical Learning (exclusion: MIE1624H)
- MIE 1624H Introduction to Data Science and Analytics (exclusion: MIE1626H)
- ECE 1513H Introduction to Machine Learning (exclusions: ECE421H, ECE521H1, CSC411H1/CSC2515H, ECE1504H)
- MSE1065H Application of Artificial Intelligence in Materials Design (exclusion: MSE1063)
- CHE1147H: Data Mining in Engineering
Elective Courses
Completing a minimum of three courses from the list of electives is required from the Emphasis in Data Analytics and Machine Learning. Students can also choose to complete one MEng project related to data analytics and machine learning instead. Check the corresponding department website for more information on their currently available MEng projects.
- APS 502H: Financial Engineering
- APS 1005H: Operations Research
- APS 1017H: Supply Chain Management and Logistics
- APS 1022H: Financial Engineering II
- APS 1050H: Blockchain Technologies
- APS 1051H: Portfolio Management Praxis Under Real Market Constraint
- APS 1052H: A.I. in Finance
- APS 1053H: Case Studies in AI in Finance
- APS 1080H: Introduction to Reinforcement Learning (APS 1080 cannot be used towards the ELITE Emphasis)
- Summer Course Offering: May 11 - August 10 (online synchronous) - Saturdays 9-11am
- CHE 507H: Data-based Modelling for Prediction and Control
- CHE 1108H: Numerical Methods
- CHE 1148H: Process Data Analytics
- CHE 1434H: Six Sigma for Chemical Processes
- CIV 1504H: Applied Probability and Statistics for Civil Engineering
- CIV 1506H: Freight Transportation and ITS Applications
- CIV 1507H: Public Transport
- CIV 1532H: Fundamentals of ITS and Traffic Management
- CIV 1538H: Transportation Demand Analysis
- CEM 1002H: Empirical Study of Cities
- ECE 537H: Random Processes
- ECE1504H: Statistical Learning (exclusion: CSC311, CSC2515, ECE421, ECE521, ECE1513)
- ECE 1505H: Convex Optimization
- ECE 1657H: Game Theory and Evolutionary Games
- ECE 1786H: Creative Applications of Natural Language Processing
- ECE 1779H: Introduction to Cloud Computing (ECE students only)
- ECE 1786H: Creative Applications of Natural Language Processing
- MIE 562H: Scheduling
- MIE1077H: AI Applications in Robotics III
- MIE 1413H: Statistical Models in Empirical Research
- MIE 1501H: Knowledge Modelling and Management
- MIE 1512H: Data Analytics
- MIE 1513H: Decision Support Systems
- MIE1517H: Introduction to Deep Learning
- MIE 1620H: Linear Programming and Network Flows
- MIE 1621H: NonLinear Optimization
- MIE 1622H: Computational Finance and Risk Management
- MIE 1623H: Introduction to Healthcare Engineering
- MIE1625H: Machine Learning for Medical Image Analysis
- MIE1628H: Cloud-based Data Analytics
- MIE 1653H: Integer Programming Applications
- MIE1666H: Machine Learning for Mathematical Optimization
- MIE 1721H: Reliability
- MIE 1723H: Engineering Asset Management
- MIE 1727H: Statistical Methods of Quality Assurance
- MIE1769H: AI in Automotive and Manufacturing
- MSE 1063H: Application of Artificial Intelligence in Process Metallurgy (exclusion: MSE1065)
The syllabus and schedule for the prerequisite course APS1070 is listed below. For all other courses please review the corresponding department websites for more information and course schedules.
APS 1070H syllabus (normally offered fall, winter, summer)