Industrial & Applied Mathematics BS

College of Sciences
Undergraduate major

About this program

The Industrial & Applied Mathematics Bachelor of Science degree is an innovative and flexible program, offering students the quantitative and computational knowledge, and communication skills, essential for careers in industry and further study of applied mathematics. The major builds upon and integrates coursework in mathematics, statistics, and computing, with a strong emphasis in modeling, data analysis, and oral and written technical communication. Two elective courses allow students to customize the program depending on their educational and career-related objectives. Students graduating with an Industrial & Applied Mathematics degree should be prepared to enter the Greater Twin Cities workforce with quantitative and computational problem-solving skills pertinent to financial, insurance, biomedical, and retail industries.

Student outcomes

After completing the Industrial and 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

Metro State connects you to your future. Receive information from Admissions about taking your first step toward a degree!

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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 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 Industrial & Applied Mathematics BS

More ways to earn your degree: Metropolitan State offers the flexibility you need to finish your degree. Through programs at our partner institutions, you can find a path to getting your Industrial & Applied Mathematics BS that works best for you.

About your enrollment options

Program eligibility requirements

Students expressing interest in the Industrial & 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.

The Industrial & Applied Mathematics BS is a new program, officially starting in Fall 2019. Students interested in pursuing this program should take the following steps:

  1. Speak with a faculty member in the Mathematics & Statistics Department or contact the Chair of the department (math@metrostate.edu) to learn more about the Industrial & Applied Mathematics, B.S. as well as other programs in the department to determine which program best aligns with your interests.
  2. Complete the following Pre-Major Requirements:
    • Take the following prerequisite courses: STAT 201 Statistics I, ICS 140 Introduction to Computational Thinking with Programming, MATH 210 Calculus I, and MATH 215 Discrete Mathematics.
    • Earn grades of C- or higher and a cumulative GPA of 2.5 or higher in the above prerequisite courses.
  3. Declare the Industrial & Applied Mathematics, B.S. using the online College of Sciences declaration form starting Fall 2019.

 

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

 

Course requirements

Pre-Major Foundation (16 credit)

Students must complete the following courses with grades C- or higher and with a cumulative GPA of 2.50 or higher in order to be admitted into the program.

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

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

Major Requirements (51-53 credits)

Students must complete a minimum of 20 credits in the program at Metropolitan State University.

Core Requirements (39-41 credits)

ICS 141 Programming with Objects

4 credits

Structure, design, and implementation of object-oriented computer programs. Topics include objects, classes, GUI, and layout managers. Introduction to containment, inheritance, and polymorphism. Programming projects involving multiple classes. Emphasis on methods, parameter passing, and arrays of objects. Exploration of problem-solving and algorithm-design techniques using pseudocode, Unified Modeling Language (UML) class diagrams, and simple patterns. Design of good test cases and debugging techniques.

Full course description for Programming with Objects

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.

Full course description for Regression Analysis

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

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 330 Optimization

2 credits

Optimization covers a broad range of problems that share a common goal - determining the values for the decision variables in a problem that will maximize (or minimize) some objective function while satisfying various constraints. Using a mathematical modeling approach, this course introduces mathematical programming techniques and concepts such as linear programming, sensitivity analysis, network modeling, integer linear programming, goal programming, and multiple criteria optimization. Software is used to solve real-world problems with an emphasis on interpretability of results. Applications include determining product mix, routing and logistics, and financial planning.

Full course description for Optimization

MATH 355 Introduction to Stochastic Processes

2 credits

Stochastic processes involve sequences of events governed by probabilistic laws. Many applications of stochastic processes occur in biology, medicine, psychology, finance, telecommunications, insurance, security, and other disciplines. This course introduces the basics of applied stochastic processes such as Markov chains (both discrete-time and continuous-time), queuing models, and renewal processes. Software is used to solve real-world problems with an emphasis on interpretation of results and the role of stochastic processes in management decision-making.

Full course description for Introduction to Stochastic Processes

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 430 Advanced Mathematical Modeling

3 credits

This course provides students with significant problem-solving experience through investigating complex, open-ended problems arising in real-world settings. Working in teams, students apply mathematical modeling processes to translate problems presented to them into problems that can be investigated using the mathematical, statistical, and computational knowledge and thinking they have gained from previous coursework. Significant emphasis is placed on justifying approaches used to investigate problems, coordinating the work of team members, and communicating analyses and findings to technical and non-technical audiences.

Full course description for Advanced Mathematical Modeling

Select one of the follow 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 use object-oriented computer programming related to the processing, summarization and visualization of data, which will prepare students to use data in their field of study and to effectively communicate quantitative findings. Topics will include basics in computer programming, data visualization, data wrangling, data reshaping, ethical issues with the use of data, and data analysis using an object-oriented programming language. 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 Requirements (8 credits)

Students must complete two of the following courses. At least one must have a MATH designation.

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

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 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, two-factor ANOVA models, repeated-measures designs, random and mixed effects, principle component analysis, linear discriminant analysis and cluster analysis.

Full course description for Analysis of Variance and Multivariate 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

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

PHYS 212 Calculus Based Physics II

5 credits

This is the second course of a two semester sequence covering the fundamental concepts of physics. This course covers oscillatory motion, waves, superposition and interference of waves, diffraction, electricity and magnetism, electric circuits, light, mirrors and lenses. Laboratories emphasize application of physics concepts and quantitative problem solving skills. Intended for science majors.

Full course description for Calculus Based Physics II

PHYS 351 Thermodynamics

5 credits

This course introduces the concepts of thermodynamics. Topics include the first law of thermodynamics, the second law of thermodynamics, entropy, statistical mechanics, specific heat capacities of gases and solids, efficiency and the Carnot cycle, chemical potential, chemicals and phase equilibriums, etc. Applications explored will include the behavior of gases and the operation of heat engines. Laboratories emphasize real world applications of the concepts and problem solving skills taught in this course.

Full course description for Thermodynamics

ICS 311 Database Management Systems

4 credits

Covers concepts and methods in the definition, creation and management of databases. Emphasis is placed on usage of appropriate methods and tools to design and implement databases to meet identified business needs. Topics include conceptual, logical and physical database design theories and techniques, such as use of Entity Relationship diagrams, query tools and SQL; responsibilities of data and database administrators; database integrity, security and privacy; and current and emerging trends. Use of database management systems such as MySQL. Coverage of HCI (Human Computer Interaction) topics. Development of GUI front ends to databases with application of HCI principles to provide a high level usability experience. Overlap: ICS 311T Database Management Systems.

Full course description for Database Management Systems

ICS 365 Organization of Programming Languages

4 credits

This course is a comprehensive introduction to the principal features and design of programming languages. It provides a comparative study of programming paradigms including structured programming, object-oriented programming, functional programming and logic programming. This course is a survey of programming concepts and constructs including data types, control structures, subprograms and parameter passing, nesting and scope, derived data types, input and output, and dynamically varying structures. Also covered are the principles of lexical and semantics analysis.

Full course description for Organization of Programming Languages

ICS 372 Object-Oriented Design and Implementation

4 credits

System development using the object-oriented paradigm. Programming topics include: inheritance, polymorphism, dynamic linking, generics, Graphical User Interfaces, and data serialization. Use-case and state-based approaches for the discovery of conceptual classes. Design principles including the Liskov Substitution Principle, Open Closed Principle, and Stable Dependencies Principle. Design patterns such as Factory, Iterator, Adapter, Facade, Bridge, Observer, Command, State, Composite, Singleton, and Mediator. Employment of design principles, design patterns, and the Model View Controller in the design of object-oriented systems. System implementation. Refactoring. Group projects.

Full course description for Object-Oriented Design and Implementation

ICS 411 Big Data Storage and Processing

4 credits

The field of computer science is experiencing a transition from processing-intensive to data-intensive problems, wherein data is produced in massive amounts by large sensor networks, simulations, and social networks. Efficiently extracting, interpreting, and learning from these very large data sets need different storage and processing requirements compared to traditional business applications that are mostly dependent on relational database management systems. These emerging data-intensive applications require heavy read/write workloads and do not need some of the stringent schema and ACID properties that are central to relational databases. To cope with these requirements, a new genre of large-scale systems, is introduced that is called NoSQL databases. The main characteristics of NoSQL databases are that they are open source, non-schema oriented, having weak consistency properties and heavily distributed over large and clusters of commodity hardware. In this course, we will cover…

Full course description for Big Data Storage and Processing

ICS 440 Parallel and Distributed Algorithms

4 credits

Covers design and development of parallel and distributed algorithms and their implementation. Topics include multiprocessor and multicore architectures, parallel algorithm design patterns and performance issues, threads, shared objects and shared memory, forms of synchronization, concurrency on data structures, parallel sorting, distributed system models, fundamental distributed problems and algorithms such as mutual exclusion, consensus, and elections, and distributed programming paradigms. Programming intensive.

Full course description for Parallel and Distributed Algorithms

ICS 460 Networks and Security

4 credits

Principles and practices of the OSI and TCP/IP models of computer networks, with special emphasis on the security of these networks. Coverage of general issues of computer and data security. Introduction to the various layers of network protocols, including physical, data link, network, and transport layers, flow control, error checking, and congestion control. Computer system strengths and vulnerabilities, and protection techniques: Topics include applied cryptography, security threats, security management, operating systems, network firewall and security measures. Focus on secure programming techniques. Programming projects.

Full course description for Networks and Security

ICS 462 Operating Systems

4 credits

Principles, techniques, and algorithms for the design and implementation of modern operating systems. Topics include operating system structures, process and thread scheduling, memory management including virtual memory, file system implementation, input output systems, mass storage structures, protection, and security. Students will implement process, memory, and file management algorithms.

Full course description for Operating Systems

FIN 390 Principles of Finance

4 credits

This course introduces the application to financial decision-making of mathematics, statistics, economic theory, and accounting procedures. The two central ideas are time value of money and the relationship between expected return and risk, and how these ideas are used to value bonds, stocks, and other financial securities, and to make capital investment decisions.

Full course description for Principles of Finance

MIS 380 Business Intelligence and Analytics

4 credits

Business Intelligence is the user-centered process of exploring data, data relationships and trends - thus helping to improve overall decision making for enterprises. This course addresses the iterative processes of accessing data (ideally stored in the enterprise data warehouse) and analyzing data in order to derive insights and communicate findings. Moreover, the course also addresses the use of software tools for analysis and visualization of data, especially report design along with the use of dashboards.

Full course description for Business Intelligence and Analytics

MIS 480 Predictive Analytics

4 credits

This course builds upon prior coursework related to analytical thinking and competence in business intelligence and analytics approaches. The course serves to advance and refine expertise on theories, approaches, tools and techniques related to prediction and forecasting in business. Students will gain practical experience in analyzing a variety of business analytics cases and scenarios using industry-standard tools and platforms. The course prepares learners to help organizations make more effective business decisions based on the gathering and analysis of data. The design and delivery of the course enables an engaged learning environment.

Full course description for Predictive Analytics

Integrative Experience: 4 credits

Students must complete 4 credits of integrative experience as listed below. Consult with academic advisor to determine an appropriate course.

STAT 480 Statistical Consulting

4 credits

This advanced workshop will give students exposure to the statistical and non-statistical issues that arise in statistical problem solving, and provide an experiential background in statistical consulting. Students will develop the knowledge, skills, and professional rapport necessary to interact with clients, including the skills necessary for communicating technical statistical content with non-statisticians.

Full course description for Statistical Consulting

STAT 350I Statistics Internship

1-4 credits

Students obtain internships in selected areas of study to gain deeper understand of knowledge, skills and the context of a given field. Site supervisors give guidance and direction to customized internship projects. Faculty members serve as liaisons between the internship sites and the university, providing information to students and potential supervisors and supervising the learning experience. Students should contact the Institute for Community Engagement and Scholarship (ICES) at Metropolitan State University for more information.

Full course description for Statistics Internship

MATH 350I Mathematics Internship

1-4 credits

Students obtain internships in selected areas of study to gain deeper understand of knowledge, skills and the context of a given field. Site supervisors give guidance and direction to customized internship projects. Faculty members serve as liaisons between the internship sites and the university, providing information to students and potential supervisors and supervising the learning experience. Students should contact the Institute for Community Engagement and Scholarship (ICES) at Metropolitan State University for more information.

Full course description for Mathematics Internship