Bachelor of Science Degree
In our data-driven world, employers are increasingly looking for candidates who can handle complex analytical challenges at work. Follow a track that applies your mathematics degree to the career path that interests you. As a math major, you'll choose between specializations in business analytics, computer programming, predictive modelling or secondary education. Whichever you choose, you'll gain essential knowledge in calculus, applied statistics, programming, discrete mathematics, and linear and abstract algebra.
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Courses in the major include:
This course provides an introduction to problem solving and computer programming using the language Python. Students will analyze problems, design and implement solutions and assess the results. Topics include fundamental programming constructs such as variables, expressions, functions, control structures and lists. Emphasis is placed on numerical and data analysis for informed decision making. Prerequisite: None
This course is designed to develop the topics of differential and integral calculus. Emphasis is placed on limits, continuity, derivatives and integrals of algebraic and transcendental functions of one variable. Upon completion, students should be able to select and use appropriate models and techniques for finding solutions to derivative-related problems with and without technology. Prerequisites: Successful completion of MA104 with a grade of C- or better or placement via ALEKS Placement Assessment.
The course deepens understanding of the material and applications learned in MA205. Topics covered include applications of the definite integral to area, volume, arc length and surface area, and developing additional integration techniques including integration by parts, trigonometric integrals and substitution, partial fractions and numerical methods. Sequences introduced as series are examined using the nth term, integral, comparison, ratio and root tests for convergence. Power series and Taylor and MacLaurin series are introduced. Calculus techniques are applied to parametric and polar equations. Prerequisite: Successful completion of MA205 with a grade of C- or better.
This course provides the theoretical basis and problem-solving experience needed to apply the techniques of descriptive and inferential statistics, to analyze quantitative data, and to improve decision making over a wide range of areas. Topics covered include descriptive statistics, linear regression, data gathering methodologies and probability, as well as confidence intervals and hypothesis testing for one and two samples. Use of technology in solving and interpreting statistical problems is emphasized. Prerequisite: MA 101 or placement via ALEKS Placement Assessment
Designed to serve as a bridge from the elementary calculus to abstract mathematics. Topics included are sets, relations, functions, induction and other methods of proof, recursion, combinatorics, graph theory and algorithms. Emphasis is placed on the solution of problems and proofs. Prerequisite: Successful completion of MA206 with grade of C- or better.
This course examines systems of linear equations, matrices, determinants and vectors to motivate study of linear spaces. Theory and applications are used to explore inner product spaces, linear transformations, eigenvalues and eigenvectors. Prerequisite: Successful completion of MA315 with grade of C- or better.
Abstract Algebra is the study of the basic underlying structures that occur in mathematical systems. This course introduces the basic ideas and applications of group theory. Elementary properties of groups and functional relationships between groups are studied including cyclic, permutation and symmetric groups, cosets (including Lagrange's theorem), subgroups and normal subgroups, homomorphisms, isomorphisms and abelian groups. Prerequisite: Successful completion of both MA315 and MA 320 with grade of C- or better.
Choose one emphasis option:
- Education (13 credit hours)
- Business Analytics (12 credit hours)
- Data Science Analysis (12 credit hours)
- Programming (9 credit hours)
- Predictive Modeling (13 credit hours)