Courses

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

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.

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.

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.

A study of the fundamental principles of mathematical statistics. Topics include probability distributions and densities, expectation and moment-generating functions, functions of random variable, sampling distributions, estimation, hypothesis testing, regression and correlation, analysis of variance, nonparametric tests.