Applied Mathematics BS

College of Sciences
Undergraduate major / Bachelor of Science

faculty and students in conversation at a table

About this program

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.

Student outcomes

After completing the Applied Mathematics major, 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 expressing interest in the Applied Mathematics BS when they apply for admission to the university will be assigned a faculty advisor in the Department of Mathematics & Statistics and will be given pre-major status.

Students interested in pursuing the Applied Mathematics BS must be formally admitted into this program before Fall 2019. Students should take the following steps:

(1) Complete the following Pre-Major Requirements:

  • Take the following prerequisite courses: STAT 201 Statistics I, MATH 210 Calculus I, and MATH 211 Calculus II.
  • Earn grades of C- or higher and a cumulative GPA of 2.5 or higher in the above prerequisite courses.

(2) Declare intent to pursue the Applied Mathematics, B.S. by submitting the online College of Sciences declaration form (see "declare your major" link below) before Fall 2019.

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 may declare a major or declare an optional minor.

Future students: Apply now

Apply to Metropolitan State: Start the journey toward your Applied Mathematics BS 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 BS

Course requirements

Prerequisites

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

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.

Full course description for Statistics I

Requirements (48 credits)

Foundation (21 credits)

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.

Full course description for Computational Thinking with Programming

PHYS 211 Calculus Based Physics I

5 credits

This is the first course of a two semester sequence covering the fundamental concepts of physics. This course covers Newton's laws of motion, work, energy, linear momentum, rotational motion, gravity, equilibrium and elasticity, periodic motion, fluid mechanics, temperature, heat, and the laws of thermodynamics. Laboratories emphasize application of physics concepts and quantitative problem solving skills. Intended for science majors and general education students with strong mathematical background.

Full course description for Calculus Based Physics I

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

Core (24 credits)

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 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

Electives (4 credits)

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

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

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.

Full course description for Environmental Statistics