Quantitative Literacy provides a college level experience that focuses on the process of interpreting and reasoning with quantitative information. Students are expected to build on prior understanding of mathematical models and applications, while integrating concepts from logic, algebra, geometry, probability and statistics. Understanding the language of mathematics, developing strategies and interpreting results, are learned via a context driven approach requiring a willingness to think about quantitative issues in new ways. The three credit course meets general education quantitative literacy requirement
This course is an introduction to the theory and application of probability and statistical analysis. Both descriptive and inferential techniques will be studied, with emphasis placed on statistical sampling and hypothesis testing. Also considered will be linear regression, contingency table analysis, and decision-making under uncertainty.
Contemporary College Algebra provides students a college level academic experience that emphasizes the use of algebra and functions in problem solving and modeling, provides a foundation in quantitative literacy, supplies the algebra and other mathematics needed in partner disciplines, and helps meet quantitative needs in, and outside of, academia. Students address problems presented as real world situations by creating and interpreting mathematical models. Solutions to the problems are formulated, validated, and analyzed using mental, paper and pencil, algebraic, and technology-based techniques as appropriate. Four credit hours.
This course introduces students to the development of mathematics from ancient to modern times, with emphasis on methods and techniques of particular times and cultures. The course also explores the connections between mathematics and other types of academic or artistic thought of a specific period, as well as the influence of mathematics on various societies.
This course is intended to prepare students for MS 181 Calculus with Applications as well as providing instruction in trigonometry to support subsequent studies in physics, chemistry, and mathematics. Emphasis is on the analysis of elementary functions and modeling, including polynomial and rational functions, exponential and logarithmic functions, and trigonometric functions. Topics in analytic trigonometry and analytic geometry are also included. Four credit hours.
This course provides an introduction to single variable calculus and its application. Emphasis is on conceptual understanding of the major ideas of calculus including limits as models of approximation, derivatives as models of change, and integrals as models of accumulation. Concepts are explored by combining, comparing and moving among graphical, numerical, and algebraic representations. This course serves as a prerequisite for MS182 Calculus II. Four credit hours.
This course is a continuation of MS181 Calculus with Applications. Prepares students for subsequent studies in mathematics, science, and business. Topics include concepts and applications of numerical integration, applications of integration, antidifferentiation, function approximation, improper integrals, and infinite series. Emphasis on concepts, complementing symbolic with graphical and numerical points of view. Integrates technology to support pedagogy and computation. Four credit hours.
In this course, students will explore the structure and properties of the Integers and some natural generalizations. Topics covered include unique factorization into primes, modular arithmetic, Fermat's Little Theorem and its applications, and may also include quadratic reciprocity, simple arithmetic functions, diophantine equations, factorization methods, primality testing, and cryptography.
This course introduces basic concepts and skills needed for understanding and conducting research in the social, educational and health sciences. Students will receive a basic introduction to the fundamentals of research – what it involves, what types exist, and how to design and conduct such research. Examined are the essential terms and concepts of research necessary for students to critically evaluate research literature, develop solid research questions, and plan simple research projects. Students will acquire foundation knowledge through readings and lecture. Active engagement with the research process will occur through class participation, exercises, literature reviews, development of research questions, and creation of inquiry strategies for answering research questions.
In this the student studies the algebraic development of linear and nonlinear equations and inequalities. Topics include math of finance, analytic geometry, linear systems of equations and inequalities, matrix theory, and linear programming. This course is designed as a continuation for those students who have taken Ms 111.
This course begins with a generalized study of systems of linear equations, developing the notion of vectors and matrices. From these ideas naturally follows the study of vector spaces of dimension three or larger, including bases, eigenvalues, eigenvectors, and matrix representations of linear transformations and change of bases. Applications discussed may include computer graphics, facial recognition, (internet) search optimization, linear programming, cryptography, Leontief economic analysis.
This course provides an introduction to the notion of mathematical proof, including a variety of techniques such as proof by contradiction and proof by mathematical induction. Topics covered typically include elementary logic, set theory, number theory, or abstract algebra, although no background is assumed in any of these areas. Students learn how to write proofs using proper notation, clear and concise language, in part by multiple revisions of their own work and critiques of others’.
This course introduces abstract mathematical structures used to represent discrete objects, including sets, permutations, relations, graphs, and trees. Emphasis is on mathematical reasoning, combinatorial analysis, and algorithmic thinking. Applications of the material are selected from subject areas ranging from computer science to geography.
Differential Equations is the study of how to identify a function from equations involving the derivatives of the function. These types of equations arise naturally in a number of places, among them biological population models, radioactive decay, heat diffusion, motion. A variety of techniques will be explored, such as separation of variables, integrating factors, variation of parameters, undetermined coefficients, and the Laplace transform. This course also includes an introduction to elementary linear algebra.
This course introduces students to the rapidly growing field of cryptography, an application of algebra. Cryptography is an indispensable tool for protecting information in computer systems. This course explains the inner workings of cryptographic primitives and their applications. Topics range from classical cryptosystems, some of which are thousands of years old, to the cutting-edge area of quantum cryptography. Relevant topics from number theory will be covered as well.
This course introduces students to linear regression and statistical modeling. After a brief review of basic statistics concepts, students will study simple linear regression, multiple linear regression, regression diagnostics, transformations, model selection procedures, common difficulties encountered with regression analysis, and other topics as time permits. Statistical software will be used to summarize data sets and build models.
Biostatistics encompasses the application and use of statistical procedures for the purposes of obtaining a better understanding of variations in data and information on living systems. Students will become familiar with one, or more, statistical software packages which will have descriptive and analytic statistical capabilities as well as report writing capacity. This course will instruct students on how to use and interpret data and information through the application of the principles of statistical inference. Specific diseases and public health issues will be used as examples to illustrate the application and use of biostatistical principles.