52 courses with the subject MATH, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
MATH 100Precalculus I Support1
Support Topics for MATH 130: Real number properties, solving equations, identifying variables including units, algebraic manipulation of expressions, graphs, systems of equations.
Introduction to applications of mathematics to daily experience. Topics to include statistical analysis and interpretation, applications to business, measurement methods, and selected topics of interest (Liberal Studies C2).
Functions using multiple representations; linear, exponential, logarithmic, polynomial, and rational functions; compositions, inverses, and combinations of functions; data and modeling, applications. (Liberal Studies C2)
Functions using multiple representations; trigonometric functions, transformations, compositions, inverses, and combinations of functions; data and modeling, applications. (Liberal Studies C2).
Descriptive statistics, probability, random variables, sampling distributions, confidence intervals, one-sample and two-sample hypothesis testing, chi-square inference for two-way tables, simple linear regression, multiple linear regression, analysis of variance. Introduction to at least one statistical software package.
Differential equations of first order, first degree with applications; linear equations of higher order and numerical methods; special equations of second order.
MATH 321Reasoning about Change, Measurement, Chance, and Data for Teaching3
Mathematics of algebraic and geometric change; measurements of two-and-three-dimensional shapes; study of chance and probability; collecting, representing, and interpreting data.
Survey of mathematical software and programming languages; applications in modeling and simulation; development of algorithms that requires advanced mathematical background.
An introduction to basic concepts in linear algebra with a focus on computation and application. Topics include systems of equations, matrices, subspaces of R^n, linear transformations, eigenvalues and eigenvectors and applications.
Classical probability models, distributions of discrete and continuous random variables including binomial, Poisson, normal, and exponential random variables, mathematical expectation, Central Limit Theorem, confidence intervals and hypothesis tests for one mean and difference of two means.
Topics in financial mathematics on the second exam of the Actuarial Societies: interest rates, annuities, accumulation functions, amortization schedules, present values, future values, and bonds.
Least squares estimates of parameters in regression models, simple linear regression, multiple linear regression, hypothesis testing and confidence intervals in linear regression models, testing of models, data analysis and appropriateness of models, time series models, moving average, regression-based and/or ARIMA models, estimation, data analysis and forecasting with time series models, forecast errors and confidence intervals. Analysis of real data will be included.
The development of mathematics from a historical perspective. Topics include elementary number theory, probability, algebra, geometry, trigonometry, and calculus.
Junior Standing or permission of department head. Credits: 3
MATH 414Introduction to Secondary Mathematics Teaching Methods3
An examination of secondary mathematics curriculum and methods, strategies, and techniques of using technology to support mathematics teaching and learning.
Admission to the Professional Education Sequence and Junior Standing, or permission of instructor. Credits: 3
MATH 415Methods and Materials for Teaching Mathematics in the Secondary School3
A survey of contemporary trends and issues in teaching mathematics at the secondary level. Methods, materials, curriculum, and other current practices are included.
Admission to the Professional Education Sequence and MATH 414 . Credits: 3
MATH 420Partial Differential Equations3
Solution techniques, modeling and analysis of partial differential equations (PDEs) and boundary value problems. Techniques include separation of variables methods for second-order linear PDEs, Fourier series, Transform methods, Bessel functions, and Sturm-Liouville Theory. Additional topics may include Green’s functions, numerical methods, and method of characteristics. (Liberal Studies C2)
Topics including design of models, analysis of stability and sensitivity, optimization, programming, forecasting, differential equation models, diffusion, or wave propagation. (Liberal Studies C2).
Numerical techniques in root-finding, interpolation, differentiation and integration, ordinary differential equations, linear systems, and error analysis.
Formulation and solution of linear programming models; development of simplex method; duality theory; sensitivity analysis; software; and applications. (Liberal Studies C2).
A theoretical development of basic linear algebra concepts in abstract vector spaces. Extension of basic concepts to include inner product spaces, orthogonalization, projections, and advanced topics at the discretion of the instructor. Development of mathematical proofs pertaining to linear algebra.
Multivariate random variables, covariance and linear functions of random variables, sampling distributions, methods and properties of estimation, confidence intervals, hypothesis testing, including likelihood ratio and Neyman-Pearson lemma, chi-square tests including goodness-of-fit and analysis of contingency tables.
Advanced problem solving by means of extensive review and practice. Preparing students for Exam P of the Society of Actuaries and the Casualty Actuarial Society.
Introduction to a high-level language, exploring, cleaning, and manipulating data, data visualization, functions and basic coding, concepts of projects and code management, and data collection.
The foundation of this course is the practical construction and validation of regression models. Topics include estimation and testing, regression diagnostics and graphical assessment, an introduction to linear models including simple and multiple regression, and an exploration of advanced models such as logistic regression, weighted least squares, and generalized least squares. The course also covers methods for model selection and performance. Use of at least one statistical software package.
This course introduces the fundamental concepts, methods, and applications of machine learning. Students will explore key topics such as supervised and unsupervised learning, model training, evaluation and interpretation, and the use of algorithms for prediction, classification, clustering, and dimensionality reduction. Emphasis will be placed on both the mathematical foundations and practical implementation of machine learning techniques using modern programming tools.
( MATH 270 or MATH 370 ) and ( CS 150 or MATH 340 or ENGR 123 or instructor permission) Credits: 3
MATH 479Senior Capstone2
Independent student review of a supervisor-approved topic from mathematical literature or undergraduate research. Students present their findings both in writing and in an oral presentation to the department.
A grade of C or better in MATH 250 , ( MATH 379 or MATH 414 ) or department head approval, and Senior Standing. Credits: 2
MATH 480Senior Thesis1
A guided undergraduate research experience with a faculty mentor. Students will write a paper and give an oral presentation on a mathematics related topic. S/U grading.
