Program Overview

Given the impact of technology on every aspect of people's lives, mathematics is a critical discipline for the present as well as future generations of students. Technology is based on science, and the most successful science is based on mathematical ideas. In learning mathematics and its applications, students learn not only the language of nature, but the archetype of reasoning on which today's scientific and technological society is based.

The Mathematics Department offers a solid, flexible and innovative program in applied mathematics. Through the opening of bridges to other disciplines and a focus on topics and problems cutting across various subject fields, the mathematics major integrates both depth and breadth, providing the student with tools for success in the workforce and a solid basis for further studies in mathematics.

More information about this program

Declare Your Program

To be eligible for acceptance to the Applied Mathematics major, students must pass and have a cumulative GPA of 2.5 in MATH 210, MATH 211, and STAT 201. When this requirement is satisfied, students must submit a College of Sciences Undergraduate Program Declaration Form for Applied Mathematics Major. Consult with an advisor before enrolling in courses toward the major.

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Requirements

Courses required for your specific program are listed in the right column on this page. They include prerequisite, foundation, core and elective courses. Contact your advisor with questions concerning your degree plan.

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Course List

Prerequisites

Applied Mathematics Prerequisites Courses

  • MATH 210 Calculus I
    4 credits

    Since its beginnings, calculus has demonstrated itself to be one of humankind's greatest intellectual achievements. This versatile subject has proven useful in solving problems ranging from physics and astronomy to biology and social science. Through a conceptual and theoretical framework this course covers topics in differential calculus including limits, derivatives, derivatives of transcendental functions, applications of differentiation, L'Hopital's rule, implicit differentiation, and related rates.

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  • MATH 211 Calculus II
    4 credits

    This is a continuation of Math 210 Calculus I and a working knowledge of that material is expected. Through a conceptual and theoretical framework this course covers the definite integral, the fundamental theorem of calculus, applications of integration, numerical methods for evaluating integrals, techniques of integration and series.

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  • STAT 201 Statistics I
    4 credits

    This course covers the basic principles and methods of statistics. It emphasizes techniques and applications in real-world problem solving and decision making. Topics include frequency distributions, measures of location and variation, probability, sampling, design of experiments, sampling distributions, interval estimation, hypothesis testing, correlation and regression.

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Requirements ( 48 total credits)

Applied Mathematics Foundation Courses (20 crediets)

  • ICS 140 Computational Thinking with Programming
    4 credits

    An introduction to the formulation of problems and developing and implementing solutions for them using a computer. Students analyze user requirements, design algorithms to solve them and translate these designs to computer programs. The course also provides an overview of major areas within the computing field. Topics include algorithm design, performance metrics, programming languages and paradigms, programming structures, number representation, Boolean algebra, computer system organization, data communications and networks, operating systems, compilers and interpreters, cloud computing, data analytics, mobile computing, internet of things, and artificial intelligence) database, internet, security, privacy, ethics, and other societal and legal issues. Lab work and homework assignments involving flow charting tools and programming using a language such as Python form an integral part of the course.

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  • MATH 301 Introduction to Analysis
    4 credits

    This is an introductory course in real analysis. Starting with a rigorous look at the laws of logic and how these laws are used in structuring mathematical arguments, this course develops the topological structure of real numbers. Topics include limits, sequences, series and continuity. The main goal of the course is to teach students how to read and write mathematical proofs.

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  • MATH 310 Calculus III: Multivariable Calculus
    4 credits

    This is a continuation of Math 211 Calculus II and covers calculus as it applies to functions of several variables. Topics include vectors and plane curves, partial differentiation, curves and vectors in space, multiple integrals, vector fields, line integrals, and Stokes Theorem.

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  • MATH 320 Probability
    4 credits

    This is a calculus-based probability course. It covers the following topics. (1) General Probability: set notation and basic elements of probability, combinatorial probability, conditional probability and independent events, and Bayes Theorem. (2) Single-Variable Probability: binomial, geometric, hypergeometric, Poisson, uniform, exponential, gamma and normal distributions, cumulative distribution functions, mean, variance and standard deviation, moments and moment-generating functions, and Chebysheff Theorem. (3) Multi-Variable Probability: joint probability functions and joint density functions, joint cumulative distribution functions, central limit theorem, conditional and marginal probability, moments and moment-generating functions, variance, covariance and correlation, and transformations. (4) Application to problems in medical testing, insurance, political survey, social inequity, gaming, and other fields of interest.

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Applied Mathematics Core Courses (24 credits)

  • MATH 315 Linear Algebra and Applications
    4 credits

    The need to solve systems of linear equations frequently arises in mathematics, the physical sciences, engineering and economics. In this course we study these systems from an algebraic and geometric viewpoint. Topics include systems of linear equations, matrix algebra, Euclidean vector spaces, linear transformations, linear independence, dimension, eigenvalues and eigenvectors.

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  • MATH 340 Mathematical Modeling
    4 credits

    Mathematical modeling is the investigation of real world phenomena using mathematical tools. This course includes topics such as dynamic and stochastic modeling (differential equations and discrete-time equations), as well as optimization modeling. Applications will include problems from such areas as the physical and biological sciences, business, and industry.

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  • MATH 350 Ordinary Differential Equations
    4 credits

    This course develops the more advanced mathematical tools necessary for an in-depth analysis of dynamic models. Topics include first order differential equations, first order systems, linear systems, nonlinear systems and numerical methods.

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  • MATH 450 Operations Research
    4 credits

    The field of Operations Research studies the mathematical methods developed for solving problems in business, industry, and management science. Following a modeling approach, this course introduces selected topics such as linear programming, integer programming, game theory, Markov chains, and queuing theory.

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  • MATH 471 Abstract Algebra
    4 credits

    By extending the familiar concepts of arithmetic, this course introduces abstract algebraic structures. Topics include an introduction to number theory; group theory, including the classification of all finite abelian groups; rings, integral domains, and fields.

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  • MATH 499 Mathematics Senior Seminar
    4 credits

    This course integrates reading of the mathematical literature with presentation of student developed projects.

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Applied Mathematics Electives (4 credits)

Program note: Other upper division mathematics courses may apply with consent of advisor.

  • MATH 375 Complex Variables
    4 credits

    Starting with an introduction to the complex plane, this course covers holomorphic functions and power series, Cauchy's Theorem, contour integration and its applications.

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  • MATH 405 Partial Differential Equations
    4 credits

    This course covers the theory of initial and boundary value problems for linear parabolic, elliptic, and hyperbolic partial differential equations. Topics may include first order equations, second order equations, separation of variables, the Sturm-Liouville problem, transform methods, Green's functions, Fourier series, numerical methods and modeling applications.

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  • MATH 420 Numerical Analysis
    4 credits

    This course addresses the theory and practice of numerical methods as they apply in various areas of mathematics. Possible topics include: numerical solutions of systems of linear and nonlinear equations, interpolation, numerical differentiation and integration, numerical solution of ordinary and partial differential equations.

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  • STAT 301 Analysis of Variance
    4 credits

    This course covers fundamental to intermediate ideas of the analysis of variance (ANOVA) method of statistical analysis. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include one-factor ANOVA models, randomized block models, balanced and unbalanced two-factor ANOVA models, completely and partially nested ANOVA models, random and mixed effects, and repeated-measures designs. Completion of STAT201 (Statistics I) is a prerequisite.

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  • STAT 311 Regression Analysis
    4 credits

    This course covers fundamental to intermediate regression analysis. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include simple and bivariate linear regression, residual analysis, multiple linear model building, logistic regression, the general linear model, analysis of covariance, and analysis of time series data. Completion of STAT201 (Statistics I) is a prerequisite.

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  • STAT 321 Biostatistics
    4 credits

    This course covers fundamental and intermediate topics in biostatistics, and builds on the ideas of hypothesis testing learned in STAT 201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use SPSS to do the analyses. Topics include designing studies in biostatistics, ANOVA, correlation, linear regression, survival analysis, categorical data analysis, logistic regression, nonparametric statistical methods, and issues in the analysis of clinical trials.

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  • STAT 331 Nonparametric Statistical Methods
    4 credits

    This course covers the fundamental to intermediate ideas of nonparametric statistical analysis. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include nonparametric methods for paired data, Wilcoxon Rank-Sum Tests, Kruskal-Wallis Tests, goodness-of-fit tests, nonparametric linear correlation and regression. Completion of STAT201 (Statistics I) is a prerequisite for this course.

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  • STAT 341 Analysis of Categorical Data
    4 credits

    This course covers the fundamental to intermediate ideas of the statistical analysis of categorical data. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include analysis of 2x2 tables, stratified categorical analyses, estimation of odds ratios, analysis of general two-way and three-way tables, probit analysis, and analysis of loglinear models. Completion of STAT201 (Statistics I) is a prerequisite.

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  • STAT 353 Environmental Statistics
    4 credits

    This course covers the intermediate statistical methods in analyzing environmental and biological datasets. This course is built on the knowledge of an introductory statistics and hypothesis testing. The contents of the course include paired T-test, unpaired T-test, F-tests, one-way and two-way ANOVA, multivariate ANOVA, repeated measures, regression, principle component analysis and cluster analysis. Students will learn how to use statistical software to perform all the analyses.This course covers the intermediate statistical methods in analyzing environmental and biological datasets. This course is built on the knowledge of an introductory statistics and hypothesis testing. The contents of the course include paired T-test, unpaired T-test, F-tests, one-way and two-way ANOVA, multivariate ANOVA, repeated measures, regression, principle component analysis and cluster analysis. Students will learn how to use statistical software to perform all the analyses.

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