Analytics covers a range of methodologies, from descriptive to predictive to prescriptive approaches.
Descriptive
Information
Using statistic to analyze and understand data
Predictive
Insight
Use machine learning to forecast unknown info
Prescriptive
Decisions
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)