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Courses

A summary of each course to help with your selection.

Academic Year

Programs

Course ID

Course ID	Course
CMPT 420	CMPT 420 Special Topics - Senior Collaborative Project Course Credits: 1, 2, 3, 4, 5, 6 Students work on a major collaborative software project in a chosen area of computing science and provide a final report and presentation. At least two, and normally not more than five people, work as a team to design, code, debug, test, and document the software. Prerequisite(s): CMPT 419, a related directed study, or instructor's consent
CMPT 461	CMPT 461 Immersive Computing Course Credits: 3 This course introduces theories, techniques, and applications of immersive computing and related technologies. Students will be introduced to advanced 3D modeling/animation and virtual environment building techniques. In a semester-long project, students will develop their projects using an immersive framework to design, build, and evaluate immersive virtual environments. Prerequisite(s): CMPT 166, 231 (3,0)
CMPT 470	CMPT 470 Introduction to Bioinformatics Course Credits: 3 An overview of the interdisciplinary science of genomics, proteomics, and bioinformatics which applies the tools of information technology (computer hardware and software) to analyze biological data such as gene or protein sequences. This course examines the theory of bioinformatics as well as its practical application to biological problems using approaches such as BLAST searches, phylogenetics, and protein structure function analysis. Cross-listed: BIOT 470 or BIOL 470 Prerequisite(s): CMPT 150, 166, and 231. (3-0)
MATH 123	MATH 123 Calculus I Course Credits: 3 This course addresses functions, limits and continuity, derivatives and applications, and integrals and applications. Prerequisite(s): At least a B in BC Principles of Mathematics 12, or Pre-Calculus 12 or equivalent, or MATH 105. (4-0-1; 4-0-1)
MATH 124	MATH 124 Calculus II Course Credits: 3 Transcendental functions, integration techniques, polar co-ordinates, sequences, series, and Taylor series. Prerequisite(s): MATH 123 (0-0-0; 4-0-1)
MATH 223	MATH 223 Calculus III Course Credits: 3 Multivariate calculus. Topics include vectors, vector functions and derivatives; curves; partial and directional derivatives; Lagrange multipliers; double and triple integrals; spherical and cylindrical co-ordinates; vector integrals, Green's Theorem, and surface integrals. Prerequisite(s): MATH 124
MATH 250	MATH 250 Linear Algebra Course Credits: 3 Systems of linear equations, matrices, determinants, vector spaces, linear transformations, eigenvalues and eigenvectors, diagonalization applications, and linear programming. Prerequisite(s): MATH 123 or 150. (3,0)
MATH 333	MATH 333 Mathematics of Data Science Course Credits: 3 Foundational mathematical concepts underpinning theoretical frameworks in data science that depend on linear algebra and multivariable calculus, with applications chosen from machine learning, statistical inference, and data assimilation. Possible topics include matrix decompositions, gradient and multivariate chain rule, Lagrange multipliers and constrained optimization, maximum likelihood, and Bayesian estimation. Prerequisite(s): MATH 223, 250 NB: Not offered every year. See department chair.
NATS 483	NATS 483 Christian Perspectives in the Sciences: Computing Science Course Credits: 3 This is a liberal arts-oriented capstone course concerning the integration of Christianity with computing science and other disciplines taught within the Faculty of Natural and Applied Sciences. Christian beliefs are applied to an understanding and evaluation of modern science and technology. The course integrates elements of theology, history and philosophy of science, and specific topics where Christian faith and science intersect. This course consists of three parts: general topics in science, further exploration of topics for students within computing science, and student-led seminars in interdisciplinary groups. Prerequisite(s): One of RELS 110, 111, 112, or 160; Completion of at least 60 sem. hrs. of study by end of preceding semester and at least 12 sem. hrs. of Computing Science

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