Industrial & Applied Mathematics Minor

College of Sciences
Undergraduate minor

About this program

The Industrial and Applied Mathematics Minor is a flexible program designed to enhance the quantitative capacities of students pursuing degrees in diverse fields. The minor integrates coursework in mathematics and statistics, including mathematical modeling and statistical programming. Two elective courses allow students to customize the program depending on their educational and career-related objectives.

Student outcomes

After completing the Industrial and 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 

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Enrolling in this program

Current students: Declare this program

Once you’re admitted as an undergraduate student and have met any further admission 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 Industrial & 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 Industrial & Applied Mathematics Minor

Program eligibility requirements

The Industrial & Applied Mathematics Minor is a new program, officially starting in Fall 2019. Students interested in pursuing this program can complete the College of Sciences online declaration form starting Fall 2019.

Transfer coursework equivalency is determined by the Mathematics and Statistics Department.

Program requirements

This minor is NOT open to students pursuing the Mathematics BA or the Industrial and Applied Mathematics BS degree.

Course requirements

Minor Requirements (24-26 credits)

All coursework must be completed with a grade of C- or better. Students must complete a minimum of 8 credit hours of their minor courses at Metropolitan State University.

Foundation courses (16-18)

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

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

MATH 230 Introduction to Mathematical Modeling

2 credits

Mathematical modeling is the process of using mathematics and computational tools to gain insights into complex problems arising in the sciences, business, industry, and society. Mathematical modeling is an iterative process which involves a computational approach to the scientific method. Assumptions are established, a mathematical structure consistent with those assumptions is developed, hypotheses are produced and tested against empirical evidence, and then the model is refined accordingly. The quality of these models is examined as part of the verification process, and the entire cycle repeats as improvements and adjustments to the model are made. This course provides an introduction to both the mathematical modeling process as well as deterministic and stochastic methods that are commonly employed to investigate time-dependent phenomena.

Full course description for Introduction to Mathematical Modeling

Select one of the following courses (2 or 4 credits)

DATA 211 Data Science and Visualization

4 credits

An introduction to methods and techniques commonly used in data science. This course will provide hands-on practice of the methods, procedures, and tools used to summarize and visualize data, preparing students to use data in their field of study and in their work, and to effectively communicate quantitative findings. Topics will include visualizations to transform data into information in a variety of contexts, relational databases, ethical issues with the use of data, and using the statistical programming language R for data analysis. Students will complete a data science project.

Full course description for Data Science and Visualization

STAT 252 Statistics Programming

2 credits

This course covers advanced statistical programming techniques including data wrangling, data visualization and hypothesis testing using R. Topics of this course include R syntax, input and output in R, data visualization, interactive data graphics, data wrangling, tidy data, and hypothesis testing in R. This course builds on the knowledge learned in STAT201.

Full course description for Statistics Programming

Electives (8 credits)

Students must complete a minimum of two of the following courses.

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

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

STAT 411 Time Series Analysis and Forecasting

4 credits

A time series is a sequence of observations on a variable measured at successive points in time or over successive periods of time. This course provides an introduction to both standard and advanced time series analysis and forecasting methods. Graphical techniques and numerical summaries are used to identify data patterns such as seasonal and cyclical trends. Forecasting methods covered include: Moving averages, weighted moving averages, exponential smoothing, state-space models, simple linear regression, multiple regression, classification and regression trees, and neural networks. Measures of forecast accuracy are used to determine which method to use for obtaining forecasts for future time periods.

Full course description for Time Series Analysis and Forecasting