Emphasis in Analytics

analytics icon

Analytics covers a range of methodologies, from descriptive to predictive to prescriptive approaches.



Using statistic to analyze and understand data



Use machine learning to forecast unknown info



Use optimization to make data-driven decision

The following descriptions are adapted from IBM white paper (source).

Descriptive analytics: “What has happened?”

  • Mines data to provide information on past or current events
  • Often emphasizes effective data visualization
  • Characterized by use of key performance indicators, reports, dashboards, basic statistics

Predictive analytics: “What could happen?”

  • Uses data accumulated over time and mathematical models to predict possible future events or associations between data
  • Characterized by use of advanced statistics, forecasting, simulation, machine learning methods

Prescriptive analytics: “What should we do?”

  • Uses data (possibly predictions) and models to recommend optimal decisions
  • Modern methods account for uncertainty in data and predictions when optimizing the decision
  • Characterized by use simulation and optimization methods

Emphasis in Analytics Requirements | Updated June 2020 – Items with * are new

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 can earn an Emphasis in Analytics by successfully completing four courses from the two lists presented below.

To be admitted in the emphasis in analytics, MEng students must first successfully complete a prerequisite course APS 1070H (0.5 FCE). Subsequently, to earn the emphasis, students must successfully complete four additional half courses (2.0 FCE) from the list of core courses or elective courses. These must include at least one core course; the remaining courses must be selected from the list of elective courses. 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.

NOTE: Students who began their MEng studies prior to September 2019 can qualify for the Emphasis in Analytics using the previous requirements listed here. MEng students who start their studies September 2019 and after must meet the most recent Emphasis in Analytics requirements listed below. If you have any questions please contact gradstudies@engineering.utoronto.ca

Prerequisite Course

APS 1070H Foundations of Data Analytics and Machine Learning | This course cannot be used towards the ELITE emphasis

Offered in Fall 2021: ONLINE, Sept 7 – Dec 9, Synchronous delivery,  Section 1: Tuesdays 9-12pm | Tut 1: Thursdays 9-11am, Section 2: Tuesdays, 5-8pm, Tut 2: Thursdays, 5-7pm

Winter 2022: ONLINE, Jan 11 – April 8, Synchronous delivery, Section 1: Tuesdays 12-3pm | Tut: Thursdays 1-3pm ; Section 2: Weds, 9-12pm | Tut: Friday 9-11am

Fall 2020 Syllabus | Winter 2020 Syllabus | Summer 2020 Syllabus

Summer 2021: ONLINE, Synchronous delivery, May 11 – August 10, Tuesdays 12-3pm lecture, Thursdays 12-2pm for Tutorial session (Course add: May 10, Course drop deadline: June 14)

Core Courses (Choose 1)

MIE 1624H Introduction to Data Science and Analytics
ECE 1513H Introduction to Machine Learning (exclusion for ECE 1504H)
MSE1065H Application of Artificial Intelligence in Materials Design (exclusion for MSE1063)
CHE1147H: Data Mining in Engineering

Elective Courses

APS 502H: Financial Engineering

APS 1005H: Operations Research for Engineering Management

APS 1017H: Supply Chain Management and Logistics

APS 1022H: Financial Engineering II

APS 1040H: Quality Control for Engineering Management

APS 1050H: Blockchain Technologies

APS 1051H: Portfolio Management Praxis Under Real Market Constraint

APS 1052H: A.I. in Finance

APS 1080H: Introduction to Reinforcement Learning | Description and timetable (New as of Fall 2020)

APS1080 Summer 2021 schedule: ONLINE, Asynchronous delivery, May 8 – July 31, Saturdays, 11-12pm (office hours) *updated May 4* | (Course add: May 10, Course drop deadline: June 14) |Fall 2021 schedule: ONLINE, Sept 11 – Dec 18, Saturday 11-12pm (synchronous office hour), Asynchronous delivery | Winter 2022: ONLINE, Jan 15 – April 16, Saturday 11-12pm (synchronous office hour), Asynchronous delivery | This course cannot be used towards the ELITE Emphasis

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 (new course offered in the 2018-2019 academic year; exclusion: ECE1513)

ECE 1505H: Convex Optimization

ECE 1510H: Advanced Inference Algorithms

ECE 1657H: Game Theory and Evolutionary Games

ECE 1778H: Creative Applications for Mobile Devices

ECE 1779H: Introduction to Cloud Computing (ECE students only)

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: Big Data Science

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)


© 2020 Faculty of Applied Science & Engineering