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 an 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.
Students 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.
Students who complete the requirements of the emphasis will have it notated on their transcript.
Program Eligiblity
Graduate students registered in the following MEng programs can specialize in the Data Analytics and Machine Learning emphasis:
- Department of Chemical Engineering & Applied Chemistry (ChemE)
- Department of Civil & Mineral Engineering (CivMin)
- Department of Materials Science & Engineering (MSE)
- Department of Mechanical & Industrial Engineering (MIE)
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE)
You cannot apply to the emphasis directly as a standalone program. If you are a prospective student, you can learn more about applying to our MEng programs here.
Careers
Graduates who complete the Data Analytics and Machine Learning emphasis are employed by companies in industries including:
- banking,
- government services,
- finance
- healthcare
- manufacturing
and more.
Some of the most common first roles our graduates secure are:
- Business intelligence analyst
- Data engineer
- Data scientist
- DevOps/MLOps engineer
- Machine learning engineer
- R&D consultant
- Senior data analyst
Alumni Profiles
I chose the MEng with a Data Analysis emphasis because I wanted to combine my data skills with an engineering background that is in high demand across industries. Data analysis is such a powerful tool for making informed decisions, and I wanted to be able to leverage that in my career. Plus, the program’s focus on real-world applications and hands-on projects really appealed to me, as it offered a practical way to learn and directly apply the skills I’m gaining.
Feiting Yang, ECE MEng student
With the rapid growth of machine learning and the expansion of companies, machine learning and big data have become essential tool sets for ensuring continuous service availability and enhancing customer experiences. Mastering these areas enables me to drive data-driven strategies and strengthens my job prospects in today's competitive landscape.
Houfu Chen, ECE MEng student
Emphasis Requirements
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.
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.
Prerequisite Course
- APS1070H: Foundations of Data Analytics and Machine Learning
- Does not count towards ELITE emphasis
Core Courses
Students must complete a minimum of one core course from the list below:
- MIE1626H: Data Science Methods and Statistical Learning (exclusion: MIE1624H)
- MIE1624H: Introduction to Data Science and Analytics (exclusion: MIE1626H)
- ECE1513H: 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
Students must complete a minimum of three courses from the list below:
- APS502H: Financial Engineering
- APS1005H: Operations Research
- APS1017H: Supply Chain Management and Logistics
- APS1022H: Financial Engineering II
- APS1050H: Blockchain Technologies
- APS1051H: Portfolio Management Praxis Under Real Market Constraint
- APS1052H: A.I. in Finance
- APS1053H: Case Studies in AI in Finance
- APS1080H: Introduction to Reinforcement Learning (APS1080 cannot be used towards the ELITE Emphasis)
- CHE507H: Data-based Modelling for Prediction and Control
- CHE1108H: Numerical Methods
- CHE1148H: Process Data Analytics
- CHE1434H: Six Sigma for Chemical Processes
- CIV1504H: Applied Probability and Statistics for Civil Engineering
- CIV1506H: Freight Transportation and ITS Applications
- CIV1507H: Public Transport
- CIV1532H: Fundamentals of ITS and Traffic Management
- CIV1538H: Transportation Demand Analysis
- CIV1599H: Analytics for Transit and Mobility Systems (added as of Summer 2024)
- CEM1002H: Empirical Study of Cities
- ECE537H: Random Processes
- ECE1504H: Statistical Learning (exclusion: CSC311, CSC2515, ECE421, ECE521, ECE1513)
- ECE1505H: Convex Optimization
- ECE1657H: Game Theory and Evolutionary Games
- ECE1786H: Creative Applications of Natural Language Processing
- ECE1779H: Introduction to Cloud Computing (ECE students only)
- ECE1786H: Creative Applications of Natural Language Processing
- MIE562H: Scheduling
- MIE1077H: AI Applications in Robotics III
- MIE1413H: Statistical Models in Empirical Research
- MIE1501H: Knowledge Modelling and Management
- MIE1512H: Data Analytics
- MIE1513H: Decision Support Systems
- MIE1517H: Introduction to Deep Learning
- MIE1620H: Linear Programming and Network Flows
- MIE1621H: NonLinear Optimization
- MIE1622H: Computational Finance and Risk Management
- MIE1623H: Introduction to Healthcare Engineering
- MIE1625H: Machine Learning for Medical Image Analysis
- MIE1628H: Cloud-based Data Analytics
- MIE1653H: Integer Programming Applications
- MIE1666H: Machine Learning for Mathematical Optimization
- MIE1721H: Reliability
- MIE1723H: Engineering Asset Management (no longer offered as of fall 2024)
- MIE1727H: Statistical Methods of Quality Assurance
- MIE1769H: AI in Automotive and Manufacturing
- MSE1063H: Application of Artificial Intelligence in Process Metallurgy (exclusion: MSE1065)
Explore all MEng emphases:
Questions?
If you have questions about the Data Analytics & Machine Learning emphasis, please contact David Duong, Engineering Graduate Affairs Officer: d.duong@utoronto.ca.
For more information, visit the MEng emphases FAQ page »