Courses

An elementary introduction to computing science and programming as a problem-solving tool. Fundamental concepts and terminology of computing science will be introduced. Programming skill will be obtained by using a high-level language. Topics will include: abstraction, data types and control structures, fundamental algorithms and pseudocode, computability and complexity, and computer architecture.

A rigorous introduction to computing science and computer programming. Students will learn in- depth programming concepts by seeing how object oriented (OO) concepts are employed in the design and writing of code in a variety of notations. The emphasis is on the theory of OO programming and design of solutions, as well as implementation using an OO language such as Java or C++.

This course addresses functions, limits and continuity, derivatives and applications, and integrals and applications.

Transcendental functions, integration techniques, polar co-ordinates, sequences, series, and Taylor series.

An introduction to those branches of pure mathematics which are most commonly used in the study of Computing Science and/or have other practical applications. Topics include logic, proofs, switching circuits, set theory, induction, functions, languages, finite automata, combinatorics, and algebraic structures.

An introduction to the theory and application of probability and statistics for students who have experience with calculus. Topics include data collection, descriptive statistics, probability, random variables and standard distributions, central limit theorem, hypothesis tests, interval estimates, and linear regression. Computer software will be used to display, analyze, and simulate data. The focus will be on biostatistics with applications using data from the life sciences.

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.

Systems of linear equations, matrices, determinants, vector spaces, linear transformations, eigenvalues and eigenvectors, diagonalization applications, and linear programming.

Examines mathematical techniques for understanding place-based environmental issues, analyzing situations, and taking appropriate action. Students will apply methods for collecting, understanding, and displaying data effectively, including gathering environmental data outdoors, then make conclusions using basic inferential statistics. Students will also examine the use of stock-flow systems and other mathematical tools to model interacting populations, energy, pollution spread, recycling, and financial networks.