Emphasis in Data Analytics and Machine Learning (formerly named Analytics)

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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.

A prior revision suggested that students could substitute a MEng Project for three elective courses. However, to clarify, this is inaccurate; students are required to complete three elective courses from the list provided below. 


[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.

  1. MIE 1626H Data Science Methods and Statistical Learning (exclusion: MIE1624H)
  2. MIE 1624H Introduction to Data Science and Analytics (exclusion: MIE1626H)
  3. ECE 1513H Introduction to Machine Learning (exclusions: ECE421H, ECE521H1, CSC411H1/CSC2515H, ECE1504H)
  4. MSE1065H Application of Artificial Intelligence in Materials Design (exclusion: MSE1063)
  5. 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. 

  1. APS 502H: Financial Engineering
  2. APS 1005H: Operations Research
  3. APS 1017H: Supply Chain Management and Logistics
  4. APS 1022H: Financial Engineering II
  5. APS 1050H: Blockchain Technologies
  6. APS 1051H: Portfolio Management Praxis Under Real Market Constraint
  7. APS 1052H: A.I. in Finance
  8. APS 1053H: Case Studies in AI in Finance
  9. 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
  10. CHE 507H: Data-based Modelling for Prediction and Control
  11. CHE 1108H: Numerical Methods
  12. CHE 1148H: Process Data Analytics
  13. CHE 1434H: Six Sigma for Chemical Processes
  14. CIV 1504H: Applied Probability and Statistics for Civil Engineering
  15. CIV 1506H: Freight Transportation and ITS Applications
  16. CIV 1507H: Public Transport
  17. CIV 1532H: Fundamentals of ITS and Traffic Management
  18. CIV 1538H: Transportation Demand Analysis
  19. CIV1599H: Analytics for Transit and Mobility Systems (added as of Summer 2024)
  20. CEM 1002H: Empirical Study of Cities
  21. ECE 537H: Random Processes
  22. ECE1504H: Statistical Learning (exclusion: CSC311, CSC2515, ECE421, ECE521, ECE1513)
  23. ECE 1505H: Convex Optimization
  24. ECE 1657H: Game Theory and Evolutionary Games
  25. ECE 1786H: Creative Applications of Natural Language Processing
  26. ECE 1779H: Introduction to Cloud Computing (ECE students only)
  27. ECE 1786H: Creative Applications of Natural Language Processing
  28. MIE 562H: Scheduling
  29. MIE1077H: AI Applications in Robotics III
  30. MIE 1413H: Statistical Models in Empirical Research
  31. MIE 1501H: Knowledge Modelling and Management
  32. MIE 1512H: Data Analytics
  33. MIE 1513H: Decision Support Systems
  34. MIE1517H: Introduction to Deep Learning
  35. MIE 1620H: Linear Programming and Network Flows
  36. MIE 1621H: Non­Linear Optimization
  37. MIE 1622H: Computational Finance and Risk Management
  38. MIE 1623H: Introduction to Healthcare Engineering
  39. MIE1625H: Machine Learning for Medical Image Analysis
  40. MIE1628H: Cloud-based Data Analytics
  41. MIE 1653H: Integer Programming Applications
  42. MIE1666H: Machine Learning for Mathematical Optimization
  43. MIE 1721H: Reliability
  44. MIE 1723H: Engineering Asset Management (no longer offered as of Fall 2024)
  45. MIE 1727H: Statistical Methods of Quality Assurance
  46. MIE1769H: AI in Automotive and Manufacturing
  47. 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)