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Mathematics BA

College of Sciences / Mathematics and Statistics
Undergraduate major / Bachelor of Arts

About The Program

The Mathematics Bachelor of Arts program at Metro State offers rigorous study in mathematics that integrates both depth and breadth. The bachelor’s degree in mathematics program provides students with a strong undergraduate foundation in mathematics essential for graduate studies in pure or applied mathematics and preparation for innovative applications of mathematics in a variety of careers. Compared to the Industrial and Applied Mathematics B.S., this is a more traditional mathematics major, with a greater emphasis on theory.

Student outcomes

After completing the Mathematics B.A., students will be able to:

  • Read and construct mathematical arguments and proofs.
  • Apply mathematical knowledge in both familiar and new situations.
  • Communicate depth and breadth of mathematical knowledge, both orally and in writing.
  • Apply analytical and theoretical skills to model and solve mathematical problems.
  • Communicate and assimilate mathematical information effectively.

 

How to enroll

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 Mathematics BA 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 Mathematics BA

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 Mathematics BA that works best for you.

About your enrollment options

Program eligibility requirements

Students expressing interest in the Mathematics Bachelor of Arts when they apply for admission to the university will be assigned a faculty advisor in the Department of Mathematics & Statistics and will be given premajor status.

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 Mathematics BA as well as other programs in the department to determine which program best aligns with your interests.
  2. Complete the following Premajor 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 Mathematics BA using the online College of Sciences Undergraduate Declaration form.

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

Courses and Requirements

SKIP TO COURSE REQUIREMENTS

Students must complete the premajor courses with grades C- or higher and with a cumulative GPA of 2.50 or higher in order to be admitted into the program. Students must complete a minimum of 20 credits in the program at Metropolitan State University.

Major Requirements

+ Premajor Foundation (16 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

This course introduces fundamental concepts in computer programming and the development of computer programs to solve problems across various application domains. Topics include number systems, Boolean algebra, variables, decision-making and iterative structures, lists, file manipulation, and problem deconstruction via modular design approaches. Lab work and homework assignments involving programming using a language such as Python form an integral part of the course.

Full course description for Computational Thinking with Programming

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

+ Core (32 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

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

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

+ Electives (8 credits)

Take 8 credits from the courses listed below.

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

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

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

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

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

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

This course addresses the theory and practice of using algorithms and computer programming to solve mathematical problems. Possible topics include roundoff and truncation errors, solution of nonlinear equations, systems of linear and nonlinear equations, interpolation and approximation, numerical differentiation and integration, numerical solution of ordinary differential equations.

Full course description for Computational Mathematics

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