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 

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.

Prerequisite Course

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

Offered in Fall 2023: Sept 11 – Dec 6
Section 1: Tuesdays, 10-1pm, in SS2108 | Tutorial 1: Tuesdays, 2-4pm in SU B120
Section 2: Wednesdays 4-7pm in GB119 | Tutorial 2: Thursdays, 5-7pm in GB220

Winter 2024: Jan 12 – April 2

Section 1: Friday, 9-1030am in GB119, 1pm-2:30pm in MS2170 | Tutorial 1: Wednesdays, 5-7pm in GB248

Section 2: Friday 10:30am-12pm in GB119, 2:30pm-4pm in MS2170 | Tutorial 2: Thursdays, 10am-12pm in GB221


Summer 2023: In-Person, May 3 – July 20,   | Lec: Wed. 6-9pm, Office Hours 5-6pm on Wed  (BA1170) | Tut: Thurs. 4-6pm (SF3202)

Core Courses (Choose 1)


MIE 1626H Data Science Methods and Quantitative Analysis (exclusion: MIE1624H)
MIE 1624H Introduction to Data Science and Analytics (exclusion: MIE1626H)
ECE 1513H Introduction to Machine Learning (exclusion ECE421H, ECE521H1, CSC411H1/CSC2515H, ECE1504H)
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 1053H: Case Studies in AI in Finance

APS 1080H: Introduction to Reinforcement Learning | Course Description

APS1080 Summer 2023 schedule: ONLINE, Synchronous delivery, May 6 – Aug 5, Saturdays, 9:00am-10:30am

Fall 2023 schedule: ONLINE, Sept 16 – Dec 9, Saturdays 9-10:30am (synchronous delivery) | This course cannot be used towards the ELITE Emphasis

Winter 2024: ONLINE, Jan 13 – April 6, Saturdays 9-10:30am (synchronous 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 1786H: Creative Applications of Natural Language Processing

ECE 1778H: Creative Applications for Mobile Devices

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 (formerly 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)