Mathematics: Course List
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- Prerequisites
- General Education
- Writing Emphasis
- Travel Course
- Repeatable
- Additional Fees
Intended as a preparation for Math 101. Topics include: properties of real numbers, exponents and polynomials, simplifying variable expressions, linear equations and inequalities, factoring, graphing, and basic quadratic equations. Offered on a pass/no credit, non-degree credit basis only.
Properties of the real numbers; solving linear and quadratic equations and inequalities; polynomials; fractional expressions and equations; exponents, powers and roots; systems of linear equations.
P: Math 094 or Math Placement of Math 101 or greater.
The real number system; inequalities; functions and their inverses; exponential and logarithmic functions; trigonometric and inverse trigonometric functions; complex numbers; polynomial and rational functions; systems of equations.
P: Math 101 with at least a C grade or transfer cse Math 004 or Math Placement of Math 104 or greater.
Basic concepts and techniques of differential and integral calculus; Applications in the fields of accounting, economics, finance and management.
P: Math 101 with at least a C grade or transfer cse Math 004 or Math Placement of Math 104 or greater.
Differential and integral calculus of the elementary functions with associated analytic geometry; transcendental functions; techniques of integration; application; sequences and series.
P: Math 104 with at least a C grade or Math Placement of Math 202 or greater.
Differential and integral calculus of the elementary functions with associated analytic geometry; transcendental functions; techniques of integration; application; sequences and series.
P: Math 202 with at least a C grade.
Real-valued functions of several variables; tangent and normal lines; chain rule for partial derivatives; extrema; least squares method; higher-ordered derivatives; integration; polar and cylindrical coordinates; spherical coordinates; vector fields; line integrals; physical applications.
P: Math 203 with at least a C grade.
Descriptive and inferential statistics; frequency distributions; graphical techniques; measure of central tendency and of dispersion; probability regression correlation, analysis of count data, analysis of variance. Credit will not be granted for both Math 260 and (Bus Adm 215 or Comm Sci 205).
P: Math 101 with at least a C grade or Math Placement of Math 101/260 or greater. Credit will not be granted for both Math 260 and (Bus Adm 215 or Comm Sci 205).
Foundations of mathematics, particularly those concepts common to the mathematics curriculum of elementary schools. Explores the processes of abstraction, symbolic representation, notational manipulation and modeling in all arithmetic contexts; examines non-arithmetic topics such as geometry, probability, statistics, algebra, and programming concepts.
P: Full admission to EDUC.
Foundations of mathematics, particularly those concepts common to the mathematics curriculum of elementary schools. Explores the processes of abstraction, symbolic representation, notational manipulation and modeling in all arithmetic contexts; examines non-arithmetic topics such as geometry, probability, statistics, algebra, and programming concepts. May not be taken on a pass/no credit basis.
P: Full Admission to EDUC
Travel courses are conducted to various parts of the world and are led by one or more faculty members. May be repeated to different locations.
P: cons of instr & prior trip arr & financial deposit.
- Gen Ed: World Culture
- Travel Course
- Course is repeatable for credit.
Solutions and applications of first and higher order linear differential equations; the meanings of existence and uniqueness theorems; nonlinear differential equations; modeling physical and biological systems.
P: Math 203 with at least a C grade.
This course deals with the construction of detailed proofs of mathematical theorems within the context of the fertile fields of Number Theory and Topology.
P: Math 202 with at least a C grade; REC: Math 203.
Matrices and vector space concepts. Systems of linear equations, matrices, determinants, vectors in two-and three-space, vector spaces, linear transformations, eigenvalues, and eigenvectors; positive-definite matrices, normal forms, the principal axis theorem, applications.
P: Math 203 with at least a C grade.
Matrices and vector space concepts. Systems of linear equations, matrices, determinants, vectors in two-and three-space, vector spaces, linear transformations, eigenvalues, and eigenvectors; positive-definite matrices, normal forms, the principal axis theorem, applications.
P: Math 320 with at least a C grade.
A course in the basic ideas of classical real analysis. Sets, functions, real numbers, limits, Euclidean space, topology of Euclidean space, continuity and uniform continuity, uniform convergence, and function spaces and their applications.
P: Math 209 with at least a C grade and 314 with at least a C grade.
Differentiable mappings, the inverse and implicit function theorems and related topics, integration on Euclidean space, Fubini's theorem and the change of variables formula, and Fourier Analysis.
P: Math 323 with at least a C grade.
Groups, rings, and fields as organizing ideas. Basic structure theorems. Applications.
P: Math 314 with at least a C grade and 320 with at least a C grade.
Analytical and numerical optimization techniques; linear, nonlinear, integer, and dynamic programming. Techniques applied to problems of water, forest, air and solid-waste management.
P: Math 320 with at least a C grade or conc enr.
Probability as a mathematical system, with applications; basic probability theory; combinatorial analysis; distribution functions and probability laws; mean and variance of a probability law; expectation related probability laws; random variables.
P: Math 209 with at least a C grade.
Sample moments and their distributions; tests of hypotheses; point and interval estimation; regression and linear hypotheses; nonparametric methods; sequential methods.
P: Math 320 with at least a C grade and 360 with at least a C grade.
Intuitive and deductive introductions to Euclidean, affine, hyperbolic, spherical, elliptic and projective geometries.
P: Math 314 with at least a C grade.
Algebra and geometry of complex numbers; analytic functions, elementary transformations, integration, Taylor and Laurent series, contour integration, residues, conformal mapping.
P: Math 209 with at least a C grade.
Fundamental concepts and techniques of discrete and continuous dynamical systems; asymptotic behavior, structural stability, elementary bifurcations, strange attractors, fractals, chaos. Applications to physical and biological systems.
P: Math 209 with at least a C grade and 320 with at least a C grade; and 305 with at least a C grade or conc enr.
Statistical theory and practice underlying the design of scientific experiments, and methods of analysis. Replication, randomization, error, linear models, least squares, crossed and nested models, blocking, factorial experiments, Latin squares, confounding, incomplete blocks, split-plots.
P: Math 202 with at least a C grade; and Math 260 with at least a C grade or Bus Adm 215 with at least a C grade.
Principles and practice in the analysis of multivariate data. Correlation, partial correlation, principle components, factor analysis discriminate functions, canonical correlation, cluster analysis, multidimensional scaling. Emphasis on computer analysis of actual data.
P: Math 202 with at least a C grade and 320 with at least a C grade; and Math 260 with at least a C grade or Bus Adm 215 with at least a C grade.
Techniques for fitting linear regression models are developed and applied to data. Topics include simple linear regression, multivariate regression, curvilinear regression and linearizable models.
P: Math 260 with at least a C grade or Bus Adm 215 with at least a C grade; and Math 202 with at least a C grade and 320 with at least a C grade; REC: knowledge of Excel.
This course brings together students and professors who have a mutual interest in some topic not otherwise available among the usual mathematics and statistics offerings.
- Course is repeatable for credit.
Supervised practical experience in an organization or activity appropriate to a student's career and educational interests. Internships are supervised by faculty members and require periodic student/faculty meetings.
P: jr st.
- Course is repeatable for credit.
Independent study is offered on an individual basis at the student's request and consists of a program of learning activities planned in consultation with a faculty member. A student wishing to study or conduct research in an area not represented in available scheduled courses should develop a preliminary proposal and seek the sponsorship of a faculty member. The student's advisor can direct him or her to instructors with appropriate interests. A written report or equivalent is required for evaluation, and a short title describing the program must be sent early inthe semester to the registrar for entry on the student's transcript.
P: fr or so st with cum gpa > or = 2.50; or jr or sr st with cum gpa > or = 2.00.
- Course is repeatable for credit.
Travel courses are conducted to various parts of the world and are led by one or more faculty members. May be repeated to different locations.
P: cons of instr & prior trip arr & financial deposit.
- Gen Ed: World Culture
- Travel Course
- Course is repeatable for credit.
Analytical and numerical optimization techniques; linear, nonlinear, integer, and dynamic programming. Techniques applied to problems of water, forest, air and solid-waste management.
P: gr st.
Statistical theory and practice underlying the design of scientific experiments, and methods of analysis. Replication, randomization, error, linear models, least squares, crossed and nested models, blocking, factorial experiments, Latin squares, confounding, incomplete blocks, split-plots.
P: gr st and intro stats cse.
Principles and practice in the analysis of multivariate data. Correlation, partial correlation, principle components, factor analysis discriminate functions, canonical correlation, cluster analysis, multidimensional scaling. Emphasis on computer analysis of actual data.
P: gt st and intro stats cse.
Techniques for fitting linear regression models are developed and applied to data. Topics include simple linear regression, multivariate regression, curvilinear regression and linearizable models.
P: gr st.
Sorry, this course does not have a description.
P: gr st.
- Course is repeatable for credit.