Applied Mathematics Minor

College of Sciences
Undergraduate minor

Math faculty at whiteboard speaking to two students

About this program

Mathematics plays an important role in the modern world: logical reasoning is crucial in everyday life, calculus-based mathematics lays the foundation for natural sciences and engineering, and probability and statistics provide insightful understanding of data and trend. Metropolitan State's Applied Math minor offers opportunities for students to gain the ability to utilize mathematics at a high level. Students are required to complete the calculus sequence and two elective courses from a broad range of course offerings in mathematics and statistics.

The Mathematics Department offers a solid, flexible and innovative curriculum 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 minor integrates both depth and breadth, providing the student with tools for success in the workforce and in their respective fields of study.

Study within the field of applied math develops skills such as:

  • Use of calculus-based mathematics Use of computing technology in applied math
  • Advanced mathematical modeling
  • Analysis of data and trend Logical thinking

At least 4 of the 20 credits for the Applied Mathematics Minor must be completed at Metropolitan State University.

Student outcomes

After completing the Applied Mathematics Minor, students will be able to:

  • Use mathematical and statistical knowledge to formulate appropriate models and problem-solving approaches in diverse contexts
  • Utilize computing skills for problem-solving, data analysis, and visualization  
  • Effectively communicate problem-solving methods and findings

Enrolling in this program

Program eligibility requirements

Students interested in pursuing the Applied Mathematics Minor must be formally admitted into this program before Fall 2019. To be admitted, students must submit the online College of Sciences declaration form (see "declare a minor" link below)

Current students: Declare your program

Once you’re admitted as an undergraduate student and have met any further requirements your chosen program may have, you declare your major or declare a minor.

Future students: Apply now

Apply to Metropolitan State: Start the journey toward your Applied Mathematics Minor now. Learn about the steps to enroll or, if you have questions about what Metropolitan State can offer you, request information, visit campus or chat with an admissions counselor.

Get started on your Applied Mathematics Minor

Course requirements

Requirements (19-20 credits)

Core (12 credits)

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.

Full course description for Calculus I

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.

Full course description for Calculus II

Electives (8 credits)

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.

Full course description for Introduction to Analysis

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.

Full course description for Probability

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.

Full course description for Mathematical Modeling

MATH 370 Modern Geometry

4 credits

This course goes beyond the Euclidean Geometry typically taught in high schools. This is a modern approach to geometry based on the systematic use of transformations. It includes a study of some advanced concepts from Euclidean Geometry and then proceeds to examine a wide variety of other geometries, including Non-Euclidean and Projective Geometry. A working knowledge of vectors, matrices, and multivariable calculus is assumed.

Full course description for Modern Geometry

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.

Full course description for Numerical Analysis

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.

Full course description for Operations Research

STAT 301 Analysis of Variance and Multivariate Analysis

4 credits

This course covers introductory and intermediate ideas of the analysis of variance (ANOVA) method of statistical analysis. The course 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 statistical software to do the analyses. Topics include one-factor ANOVA models, randomized block models, two-factor ANOVA models, repeated-measures designs, random and mixed effects, analysis of covariance, principle component analysis, and cluster analysis. Completion of STAT 201 Statistics I is a prerequisite.

Full course description for Analysis of Variance and Multivariate Analysis

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.

Full course description for Regression Analysis

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.

Full course description for Biostatistics

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.

Full course description for Nonparametric Statistical Methods

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.

Full course description for Analysis of Categorical Data

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.

Full course description for Environmental Statistics