Mathematics Education Major
Love math? Prepare to teach mathematics and you'll have the chance to share your enthusiasm to help others learn essential skills and achieve mathematical success.
A degree in mathematics education prepares graduates to teach mathematics in public or private middle or high schools. Graduates often seek advanced degrees from Saint Mary's in special education, literacy, educational administration, curriculum and instruction, school counseling, or school psychology.
High School Preparation
High school coursework that will support a student in his or her pursuit of a mathematics education degree includes Calculus, Computer Science, Discrete Math, Psychology and Statistics.
Enhance Your Experience
Degree candidates can take skill-building courses in areas like mathematics, reading, writing and/or study skills, and sometimes pursue a minor in subjects such as psychology, physics, or computer science.
A. All of the following
(either M148 & M149 or M151):
This course, followed by
This course completes the two-semester sequence that begins with
This course provides an introduction to the differential and integral calculus. Topics include: the concepts of function, limit, continuity, derivative, definite and indefinite integrals, and an introduction to transcendental functions. Credit is not granted for this course and
This course is a continuation of
This course continues the development of Calculus from
B. All of the following:
This course provides an introduction to techniques and applications of linear algebra. Topics include: systems of linear equations, matrices, determinants, Euclidean n-space, real vector spaces, basis and dimension, linear transformations, inner products, and eigenvalues and eigenvectors.
This course looks at topics central to further study in mathematics. Topics include symbolic logic, especially as it applies to mathematical proof; methods of mathematical proof such as direct proof, indirect proof, proof by induction; use and meaning of mathematical quantifiers and predicates; sets; relations; equivalence relations and partitions; order relations; functions and their properties; and complex numbers. A junior assessment test is administered as part of this course.
This calculus-based course is designed to provide mathematics majors and minors with an introduction to the mathematical underpinnings of statistics. Topics include: probability axioms, probability, Bayes' Theorem, random variables, discrete and continuous probability distributions, and expected value.
This course provides an introduction to algebraic structures. Topics include: groups, subgroups, quotient groups, group homomorphisms, rings, ideals, and fields.
This course provides a rigorous treatment of topics in calculus. Topics include: sequences, functions, limits, continuity, derivatives, and integration.
This course consists of student presentations from mathematics, mathematical modeling, mathematics education, or statistics. Each student chooses a topic in consultation with the instructor, does appropriate background reading, and prepares an oral presentation and written paper on the topic. A senior assessment test is administered as part of this course.
This course is designed to provide the basic ideas and techniques of statistics. Topics include: descriptive and inferential statistics, an intuitive introduction to probability, estimation, hypothesis testing, chi-square tests, regression and correlation. This course makes significant use of appropriate technology. Topics in this course are treated at a higher mathematical level than they are treated in
C. Both of the following:
This course is required for the Mathematics Education major. The course is designed to be an introduction to the foundations of geometry. Topics include: Euclidean geometry, non-Euclidean geometry, projective geometry, and geometric transformations.
This course is required for the Mathematics Education major, providing an introduction to techniques and applications of operations research. Topics include: linear programming, game theory, queuing theory, Markovian decision processes, and decision theory.
D. Two of the following courses
Two of the following courses; or one of the following courses and one course in another field using mathematics with the approval of the major advisor and the department chair.
This course provides an introduction to combinatorial and graph theoretical techniques in mathematics. It is also designed for students in computer science. Topics include: sets, functions, combinatorial techniques, graph theory, searching algorithms, and trees.
This course provides an introduction to elementary number theory. Topics include: divisibility, prime and composite numbers, congruences, arithmetical functions, primality testing, factorization techniques, and applications to cryptography.
This course provides an introduction to the theory, methods, and applications of ordinary differential equations. Topics include: first order differential equations, linear differential equations with constant coefficients, and systems of differential equations.
This course provides an introduction to the theory and methods of numerical analysis. Topics include: numerical methods for solving linear and nonlinear equations, polynomial approximation of functions, numerical integration and differentiation, numerical approximation to solutions of differential equations, direct and iterative methods for solving systems of equations.
This course serves physics majors as well as those mathematics majors whose area of interest is analysis. Topics include: Fourier series, the complex numbers, analytic functions, and derivatives and integrals of complex functions. Other topics may include Laurent series and residues, partial differential equations and boundary value problems.
This course provides an introduction to the theory of functions of one complex variable. Topics include: the complex numbers, the complex derivative, analytic functions, power series, complex integration, Cauchy's Theorem and Cauchy's Integral Formula, Laurent series, and residues and poles.
This course is designed to provide undergraduates students with the basic computational tools and techniques needed for their study in science and mathematics. Students learn by doing projects that solve problems in physical sciences and mathematics using symbolic and compiled languages with visualization. By use of the Sage problem-solving environment and the Python programming language, the students learn programming and numerical analysis in parallel with scientific problem solving.
Special topics in mathematics may be offered depending on student interest.
This opportunity provides the student with experience in mathematical research or applications. The internship must be approved by the department chair and, depending on the nature of the internship, may be counted towards the major. Students generally are expected to give a presentation following the internship.
Selected topics in statistics may be offered depending on student interest.
This course provides students with an introduction to linear and non-linear models in statistics. Topics include: linear regression, multiple regression, one-, two-, and higher-way analysis of variance, and popular experimental designs. Real-world problems are analyzed using appropriate technology.
This course provides an introduction to the principles of the design of experiments from a statistical perspective. Topics include: Analysis of variance, covariance, randomization, completely randomized, randomized block, Latin-square, factorial, response surface methods and other designs.
This course provides a mathematical treatment of probability and statistics. Topics include: several descriptions of the concept of probability, univariate and bivariate probability distributions, joint and marginal probability distributions, covariance, hypothesis testing, estimation, data analysis, and sampling distributions.
This opportunity provides the student with experience and training in statistical techniques. The internship must be approved by the department chair and, depending on the nature of the internship, may be counted towards the major. Students usually are expected to give a presentation following the experience.
E. Either CS106 or CS110 & CS111:
This course teaches introductory programming within a problem solving framework applicable to the sciences. The course emphasizes technical programming, introductory data storage techniques, and the processing of scientific data. There is an emphasis on designing and writing correct code using an easy to learn scientific programming language such as Python. Advanced excel spreadsheet concepts will be taught and utilized during the programming process.
This course introduces students to the practice of software development. Students learn the fundamentals of programming, algorithm development, and basic design principles.
The laboratory course complements
F. Required education course work