Computer Science Supplementary Minor Concentration (B.A.) (18 credits)
Offered by: Computer Science (Faculty of Science)
Degree: Bachelor of Arts
Program credit weight: 18
Program Description
The Supplementary Minor Concentration may be taken only by students registered in the Major Concentration Computer Science or the Major Concentration Software Engineering. There may be no overlap in credits taken for this Supplementary Minor Concentration and the Major Concentration Computer Science/Software Engineering. Taken together, these constitute a program very close to the Major Computer Science offered by the Faculty of Science. Students must get their selection of courses approved by an Academic Adviser in the School of Computer Science.
Students with two programs in the same department/unit must have a third program in a different department/unit to be eligible to graduate. Please refer to the Faculty of Arts regulations for "Faculty Degree Requirements", "About Program Requirements" and "Departmental Programs" for the Multi-track System options.
Complementary Courses (18 credits)
18 credits selected from Computer Science (COMP) courses at the 300 level or above excluding COMP 396 Undergraduate Research Project..
Students may also select a maximum of 3 credits of MATH courses from the list below.
| Course | Title | Credits |
|---|---|---|
| MATH 223 | Linear Algebra. | 3 |
Linear Algebra. Terms offered: Fall 2026, Winter 2027 Review of matrix algebra, determinants and systems of linear equations. Vector spaces, linear operators and their matrix representations, orthogonality. Eigenvalues and eigenvectors, diagonalization of Hermitian matrices. Applications. | ||
| MATH 318 | Mathematical Logic. | 3 |
Mathematical Logic. Terms offered: Fall 2026 Propositional logic: truth-tables, formal proof systems, completeness and compactness theorems, Boolean algebras; first-order logic: formal proofs, Gödel's completeness theorem; axiomatic theories; set theory; Cantor's theorem, axiom of choice and Zorn's lemma, Peano arithmetic; Gödel's incompleteness theorem. | ||
| MATH 323 | Probability. | 3 |
Probability. Terms offered: Summer 2026, Fall 2026, Winter 2027 Sample space, events, conditional probability, independence of events, Bayes' Theorem. Basic combinatorial probability, random variables, discrete and continuous univariate and multivariate distributions. Independence of random variables. Inequalities, weak law of large numbers, central limit theorem. | ||
| MATH 324 | Statistics. | 3 |
Statistics. Terms offered: Fall 2026, Winter 2027 Sampling distributions, including Normal, Chi-squared, Student’s t, and F distributions. Sampling properties of the sample mean and variance. Point estimation: bias, variance, mean squared error, consistency, sufficiency, UMVUE. Method of moments and maximum likelihood estimation. One- and two-sample inference. Confidence intervals and sample size determination. Hypothesis testing, Type 1 and Type 2 errors, power, and p-values. The Neyman Pearson framework and likelihood ratio tests. Linear models used as illustrative examples, linking them to topics in estimation and hypothesis testing. One-way analysis of variance (ANOVA) and contingency tables. | ||
| MATH 340 | Discrete Mathematics. | 3 |
Discrete Mathematics. Terms offered: Winter 2027 Discrete Mathematics and applications. Graph Theory: matchings, planarity, and colouring. Discrete probability. Combinatorics: enumeration, combinatorial techniques and proofs. | ||