CS 190 - Computer Science I (3 cred.)
An introduction to software development taught in Python. Topics include control structures, I/O, functions, strings, lists, files, other data structures and basic algorithms that use them. Emphasis is placed on good problem-solving practices, testing and debugging.
CS 303 - Machine Learning (3 cred.)
A study of computer systems that learn. Topics include decision trees, concept learning, neural networks, reinforcement learning, linear and non-linear models, clustering, validation, feature selection, support vector machines and hidden Markov models with applications to the arts and sciences. Prerequisite: CS 220 Data Analytics with minimum grade of “C-”.
CS 365 - Big Data Analytics (3 cred.)
An intensive study of big data and informatics applications for digital data. Topics include text analysis using classic works and social media, numeric analysis using economic and scientific data and symbolic analysis using genomic data. Emphasis is on programming solutions to complex problems. Prerequisite: CS 220 with minimum grade of “C-”.
CS 440 - Distributed Computing for Machine Learning and Data Analysis (3 cred.)
A programming intensive introduction to distributed computing with attention to applications in machine learning and data analysis. Topic includes distributed sequential analysis methods, distributed Markov model-based methods, and distributed support vector machine-based methods. Prerequisite: CS 303 or CS 365 with a minimum grade of “C-”.
ECON 202 - Microeconomics (3 cred.)
The theory of microeconomics makes use of the tools of marginal cost-benefit analysis to provide a framework for the economic analysis of decision-making. The focus is on the choices of individual firms and consumers, and the resultant outcomes in individual markets. The social implications of the functioning of competitive markets are examined, as well as the causes of market failure and the potential roles of government in correcting them. Prerequisite: ACT math score of 19 or above; SAT math score of 500 or above; pass MATH 099; or Accuplacer Elementary Algebra test score of 85 or higher, or college-level math requirement with a minimum grade of "C-."
ECON 216 - Statistics for Business and Economics (3 cred.)
An introduction to descriptive statistics and statistical inference, with application in business, including hypothesis testing, confidence intervals, and simple regression analysis. Prerequisite: MATH 140, MATH 141, or MATH 151 with a minimum grade of ÒC-.Ó
ECON 316 - Econometrics (3 cred.)
The application of advanced statistical methods and modeling to an empirical understanding of economic issues. Combines elements of statistical reasoning with economic theory and provides an excellent opportunity to combine concepts learned in previous economics courses. Topics covered include multiple regression analysis, model specification, dummy variables, multicollinearity, heteroscedasticity, autocorrelation, limited dependent variables, simultaneity, time series, forecasting, and methodological issues. Prerequisites: ECON 201or ECON 202; and ECON 216 or MATH 213.
MATH 213 - Probability and Statistics (3 cred.)
A course in the use of statistical techniques to draw knowledge from data. Topics include exploratory data analysis, descriptive statistics, t-procedures, ANOVA, chi squared procedures, regression, and non-parametric tests. Statistical software is used extensively to analyze real data sets. Prerequisite: MATH 141 with a minimum grade of C-, or Accuplacer university-level mathematics test score of 85 or above; or instructor permission. GT-MA1
MATH 260 - Applied Linear Algebra (3 cred.)
A course in the techniques and applications of linear algebra. The core topics include solving systems of linear equations, eigenvalues and eigenvectors, matrix decomposition, the pseudoinverse and least squares approximations, and the singular value decomposition. The theory is supplemented with extensive applications and computer programming. Prerequisite: MATH 141.
MATH 313 - Statistical Modeling and Simulation (3 cred.)
A study of statistical techniques used to model and simulate stochastic processes. The core topics include linear and nonlinear multivariate models, generalized additive models, time series models with auto-correlated error, and mixed effects models. Emphasis is placed on computational techniques appropriate to large data sets and data visualization. Prerequisites: MATH 213 or ECON 216, MATH 260, CS190.
Faculty & Staff
Instructor of Mathematics
Office Location: Hurst Hall 216
Professor of Mathematics, Chair of the Department of Mathematics & Computer Science
Office Location: Hurst Hall 210
Professor of Mathematics
Office Location: Hurst Hall 216
Lecturer of Mathematics
Office Location: Hurst Hall 108
Associate Professor of Mathematics
Office Location: Hurst Hall 112
Lecturer of Math
Office Location: Hurst Hall 114
Office Location: Hurst Hall 106
Lecturer of Mathematics
Office Location: Hurst Hall 110
John Peterson Memorial Scholarship In Computer Science
- Students majoring in Computer Information Science
- Completed a minimum of 12 credit hours at Western, 3 of which can be applied toward their majors
- Must have minimum 3.0 GPA
- Plan on enrolling in at least nine credits
This scholarship is provided by Stephen Watson.
Contact the Mathematics & Computer Science Department for application and deadline information.
970.943.2015 | Hurst Hall 128
- Conferences: Students and faculty travel to two conferences each year.
- Seminars: Faculty show what they have been working on and students present their research projects.
- Tutoring Jobs: Available to students interested in teaching others and mastering basic principles.
Reach out for more information about the program.