Professional Degree courses in Dentistry, Education, Law, Medicine and Theology (MTS, MDiv)
Courses offered by Continuing Studies
Graduate Studies courses
* These courses are equivalent to pre-university introductory courses and may be counted for credit in the student's record, unless these courses were taken in a preliminary year. They may not be counted toward essay or breadth requirements, or used to meet modular admission requirements unless it is explicitly stated in the Senate-approved outline of the module.
1.0 course not designated as an essay course
0.5 course offered in first term
0.5 course offered in second term
0.5 course offered in first and/or second term
1.0 essay course
0.5 essay course offered in first term
0.5 essay course offered in second term
0.5 essay course offered in first and/or second term
1.0 accelerated course (8 weeks)
1.0 accelerated course (6 weeks)
0.5 graduate course offered in summer term (May - August)
0.25 course offered within a regular session
0.25 course offered in other than a regular session
1.0 accelerated course (full course offered in one term)
0.5 course offered in other than a regular session
0.5 essay course offered in other than a regular session
A course that must be successfully completed prior to registration for credit in the desired course.
A course that must be taken concurrently with (or prior to registration in) the desired course.
Courses that overlap sufficiently in course content that both cannot be taken for credit.
Many courses at Western have a significant writing component. To recognize student achievement, a number of such courses have been designated as essay courses and will be identified on the student's record (E essay full course; F/G/Z essay half-course).
A first year course that is listed by a department offering a module as a requirement for admission to the module. For admission to an Honours Specialization module or Double Major modules in an Honours Bachelor degree, at least 3.0 courses will be considered principal courses.
Students will learn how to visualize and analyze continuous and categorical data from various domains, using modern data science tools. Concepts of distributions, sampling, estimation, confidence intervals, experimental design, inference, correlation will be introduced in a practical, data-driven way.
Programming for Data Science is intended for students with little or no background in programming. Design and analysis of algorithms and their implementation as modular, reliable, well-documented programs written in a modern programming language.
Covers three basic concepts of data science together with the corresponding techniques: Sampling to estimate properties of a population (Bootstrap), random assignment and experiments to make causal inferences (randomization test), and model selection to enable good predictions (cross-validation). Emphasizes practical data handling and programming skills in Python.
Prerequisite(s): 1.0 courses from Mathematics, Calculus, or Applied Mathematics (numbered 1000 and higher) with a minimum mark of 60%. Data Science 1000A/B (with a minimum grade of 60%) can be used to meet 0.5 of the 1.0 mathematics course requirements.
Extra Information: 2 lecture hours/week, 2 laboratory hours/week.
Mathematical background for students wanting to take Data Science 3000A/B, but missing background in linear algebra and calculus. Vector and matrix algebra, norms, linear dependence, inverses, vector spaces, eigenvectors and eigenvalues, Gradients, Hessians, basics of optimization. All concepts are explained in the context of data science examples.
Prerequisite(s): 1.0 courses from Mathematics, Calculus, or Applied Mathematics (1000 and higher) with a minimal grade of 60%. Data Science 2000A/B or Integrated Science 2002B can be used to fulfil 0.5 of the requirements.
Extra Information: 3 lecture hours/week, 1 tutorial hour/week.
Basic principles of machine learning (estimation, optimization, prediction, generalization, bias-variance trade-off, regularization) in the context of supervised (linear models, decision trees, deep neuronal networks) and unsupervised (clustering and dimensionality reduction) statistical learning techniques. The course emphasizes the ability to apply techniques to real data sets and critically evaluate their performance.
Antirequisite(s): the former Computer Science 4414A/B, the former Statistical Sciences 3850F/G, the former Software Engineering 4460A/B.