75 courses with the subject MTH, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
Developmental approach for students whose backgrounds indicate a need for further review of arithmetic and basic algebra. Mathematics laboratory required. (Credits usually do not count toward the mathematics requirements of a student's major.)
Topics include operations of real numbers, ratios, proportions, percents, order of operations, linear and quadratic equations, inequalities, graphing, operation of polynomials, roots, radicals, and system of equations. A lab component is used to reinforce the concepts of the topics introduced in class.
Emphasis on global, unifying ideas in mathematics and the connections between contemporary mathematics and modern society. Topics are selected from elementary mathematics, logic, probability and statistics, discrete systems, geometry, measurement, and consumer applications. This course satisfies the minimum general education mathematics requirement.
Emphasis on global, unifying ideas in mathematics and the connections between contemporary mathematics and modern society. Topics selected from elementary mathematics, logic, probability and statistics, discrete systems, geometry, measurement, and consumer applications. This course satisfies the minimum general education mathematics requirement.
Preparation for the pre-calculus including linear and quadratic equations, graphing, polynomials, roots, radicals, and systems of equations. (Satisfies the minimum general education mathematics requirement.)
Preparation for the pre-calculus including linear and quadratic equations, graphing, polynomials, roots, radicals, and systems of equations. (Satisfies the minimum general education mathematics requirement.)
Transition from elementary mathematics to calculus including a review of exponents, factoring, linear and quadratic equations, inequalities, functions, graphs, system of equations, exponential and logarithmic functions.
Introduction to elementary calculus including limits, continuity, differentiation, integration, and applications in business.
Thorough treatment of the modern mathematics curricula for prospective school teachers. Emphasis on sets and logic, number systems, number theory, algebra, geometry and measurement. Computer-based laboratory component with manipulatives included.
Continued treatment of the modern mathematics curricula for prospective school teachers. Emphasis on geometry and measurement.
Continued treatment of the modern mathematics curricula for prospective school teachers. Emphasis on geometry and measurement.
This course emphasizes the study of basic algebra, stressing fundamental concepts and reasoning used in mathematics and the sciences. Students are expected to bring to the course knowledge of the essentials of elementary and intermediate algebra. Emphasis is placed on those skills necessary for calculus sequences.
This course emphasizes the study of basic algebra and stresses fundamental concepts and reasoning used in mathematics, biology and chemistry. Students are expected to bring to the course knowledge of the essentials of elementary and intermediate algebra.
Extension of algebra topics and a treatment of trigonometry necessary for the study of advanced subjects in mathematics and the sciences. Preparation for the calculus sequence. Topics include exponential and logarithmic functions, trigonometric functions, trigonometric identities, and trigonometric applications necessary for the study of advanced subjects in mathematics and the sciences.
Extension of algebra topics and a treatment of trigonometry necessary for the study of advanced subjects in mathematics and the sciences. Preparation for the calculus sequence. Topics include exponential and logarithmic functions, trigonometric functions, trigonometric identities, and trigonometric applications necessary for the study of advanced subjects in mathematics and the sciences.
This is a first course in the essentials of Calculus, necessary for more advanced study in the natural sciences and mathematics. Topics include limits, continuity, derivatives and applications, antiderivatives, and the Fundamental Theorem of Calculus. The course integrates some calculus applications with computer activities.
Treatment of the essentials of calculus necessary for the study of more advanced subjects in the natural sciences and mathematics including limits, continuity, derivatives and applications, antiderivatives and the Fundamental Theorem of Calculus. The course integrates some calculus applications with computer activities.
Study of the history and development of mathematics as a vital and integral part of the history of numbers and numerals, computation, geometry, algebra, trigonometry, calculus, and modern mathematics.
Introduction to statistics including graphical data representation, basic probability concepts, sampling and expectation, confidence interval, and hypothesis testing for sample mean and proportion.
Introduction to statistics including graphical data representation, basic probability concepts, sampling and expectation, confidence interval and hypothesis testing for sample mean and proportion.
Applications of definite integrals, the calculus of transcendental functions, infinite series, and integration techniques. Some topics are integrated with computer activities.
Applications of definite integrals, the calculus of transcendental functions, infinite series, and integration techniques. Some topics are integrated with computer activities.
Investigation of calculus concepts at the intermediate level including polar coordinates, vectors, and the calculus of several variables.
This course is a continuation of Calculus II (MTH 251). The course investigates calculus concepts at the intermediate level designed for mathematics and science majors. Topics include polar coordinates, vector analysis, and the calculus of several variables on an honors level.
This course looks at fundamental topics to further study in mathematics. Topics include basic concepts of set theory, basic concepts of logic, basic concepts of algebra, methods of mathematical proof, relations and functions, the concept of limit and continuity, study of the real number set and its topology, and some topics from calculus.
Introduction to the basic concepts, techniques, and elementary applications of linear algebra including matrices, linear systems, gaussian elimination, vector spaces, linear independence, linear transformations, eigenvalues and eigenvectors.
This course is an introduction to basic concepts, techniques, and elementary applications of linear algebra. Topics to be covered are matrices, linear systems, Gaussian elimination, vector and vector spaces, linear independence, linear transformations, eigenvalues and eigenvectors, finite-dimensional spectrum theory on an honors level.
Introduction to discrete math including topics in graph theory, management science, the mathematics of social change, and statistics. Use of manipulatives and other learning tools included.
Re-examination of Euclidean plane geometry as a postulational system. Emphasis on formulating definitions and constructing valid proofs including mathematical reasoning, postulational method, finite geometries, congruence, similarity, parallelism, and construction with ruler and compass.
An introduction to modern algebra, which deals with selected algebraic structures (groups, rings, fields, etc.). The course stresses the axiomatic approach and the logic and method of proof.
Given the importance of probability and statistics in the research fields of most sciences, this course has been designed to serve as a calculus-based introduction to fundamental concepts in probability and statistics. The course places particular emphasis on the fundamental concepts of probability and presents basic statistics concepts as an extension of these concepts.
Given the importance of probability and statistics in the research fields of most sciences, this course has been designed to serve as a calculus-based introduction to fundamental concepts in probability and statistics. The course places particular emphasis on the fundamental concepts of probability and presents basic statistics concepts as an extension of these concepts.
Given the importance of probability and statistics in the research fields of most sciences, this course has been designed to serve as a calculus-based introduction to some advanced concepts in probability and statistics. Given that it is the second course in the sequence, the course also provides reinforcement of the fundamental concepts covered in MTH 351. While building upon these fundamentals, this course will place particular emphasis on the concepts of statistical inference and experimental design.
An introduction to the area of discrete mathematics that is important to computer science. Topics include logic, sets, functions and relations, algorithms, counting principles, and graph theory.
A first course in ordinary differential equations. Topics include first-order equations, higher order linear differential equations, and the Laplace transform. Applications include growth/decay models, electric circuits, and the vibrational models.
A one-semester course in the calculus of functions of several variables and vector analysis. Topics include derivatives and integrals of functions of several variables, vector fields, divergence, curl, Green's Theorem, and Lagrange Multipliers. Course includes selected applications to the physical sciences.
A junior-level introduction to applications of mathematics designed for mathematics, computer science, and engineering majors. Topics include Fourier Series, Laplace transforms, Sturm-Liouville problems, and Bessel functions.
Introduction to numerical techniques for problem solving involving the use of the computer. Topics include error analysis, solutions of one variable equations, solutions of linear and nonlinear systems of equations, iterative techniques in matrix algebra, and approximating eigenvalues.
Continuation of MTH 401. Topics include polynomial interpolation and approximation, numerical differentiation and integration, approximation theory, and numerical approaches to ordinary and partial differential equations.
Continuation of MTH 331. Topics include a more advanced discussion of groups, rings, fields, homomorphism, isomorphism, and automorphism.
Topics to be covered include single factor experiments, residuals, randomized block designs, general factorials, blocking, regression models, unbalanced data, confounding blocks, and Taguchi experiments.
Rigorous treatment of functions of one and several variables, improper integrals, sequences, infinite series, uniform convergence, and applications. Students are expected to improve their ability to work in an abstract setting using precise definitions and formal proofs and to present their work in class.
Offers a solid theoretical foundation for a careful study of the real number system and functions defined on this system. Provides the substance and basis for enabling students to understand much of traditional calculus, including proofs of many of the standard results on limits of sequences, limits of functions, continuity, uniform continuity, sequences and series of functions, integrals, and approximations.
Treats the fundamentals of analytic function theory. Topics include algebra and geometry of the complex numbers, limits, derivatives, Cauchy-Riemann equations, Cauchy's Theorem, Taylor and Laurent series, and contour integration.
This course is a continuation of Math 382, Introduction to Applied Mathematics I. It is a senior-level course containing advanced topics in mathematical and scientific applications. Topics vary but may include partial differential equations, Fourier analysis and boundary value problems, with selected applications in mathematical physics and engineering. The course integrates some applications of partial differential equations with computer activities.
This course is a continuation of Math 382, Introduction to Applied Mathematics I. It is a senior-level course containing advanced topics in mathematical and scientific applications. Topics vary but may include partial differential equations, Fourier analysis and boundary value problems, with selected applications in mathematical physics and engineering. The course integrates some applications of partial differential equations with computer activities.
Contact the department for specific course information.
Culminating course designed to review and fortify knowledge of essential mathematics concepts and to synthesize mathematical knowledge and experiences through participation in a research project of the student's choice. Results of the research are presented to peers and other interested members of the academic community. Course includes a comprehensive examination used to assess the objectives of the core mathematics courses.
Culminating sequence designed to review and fortify knowledge of essential mathematics concepts and to synthesize mathematical knowledge and experience through the completion of an approved research project. Results of the research are presented to peers and other interested members of the academic community. Course includes a comprehensive examination used to assess the objectives of the core mathematics courses.
Contact the department for specific course information.
See department for more information
See department for more information
Special contract course requested by Norfolk Schools Portsmouth, for in service teachers to acquire Math Specialist Cerification
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Special Grant Course Requested by Norfolk Public Schools & Va Beach Public School
A graduate-level introduction to advanced introduction to various graphs, trees, flows in networks, maps, walks, networks, and cycles. This course will primarily introduce all the standard graphs theory results, emphasizing its applications in Data Science. Large datasets with multiple interconnections between dataset variables can be distilled and illuminated using various graphs, trees, and networks, recognizing situations where graphs delineate a given dataset. An introduction to the tree search algorithm and solutions to four color problems is covered.
A graduate level course on topological methods for data analysis. The focus is split between basic topological theory and applications such as topological persistence, zigzag persistence, topological analysis of point clouds, Reeb graphs, and topological invariants for directed graphs.
Contact the department for specific course information
Contact the department for specific course information
A graduate level introduction to probability and statistical with emphasis towards applications in data sciences. Probabilistic and statistical methods regularly provide the foundations for data science, the methodologies included in this course will provide the students the knowledge needed in several fields as marketing, finance, and other disciplines. This course will prepare the students for modeling and understanding big data problems.
Contact the department for specific course information.
A graduate level introduction to mathematical foundations for machine learning provides a collection of tools for doing machine learning. While the theory of the tools may be technical, the emphasis is on a balance between theory and practice, with hands-on activities assigned to help the understanding of the theory.
See department for more information
Contact the department for specific course information.
This course is a continuation of linear algebra, towards topics relevant to applications as well as theoretical concepts. The course starts with a review of matrices, linear systems, subspaces, determinants, eigenvalues and eigenvectors, and orthogonal vectors. Then it introduces the basic techniques, analysis methods, and implementation details of numerical linear algebra. Emphasis will be given on the matrix computations that arise in solving linear systems, least squares problems, and eigenvalue problems. Students will demonstrate knowledge by completing a final project that demonstrates understanding of linear systems applications.
The purpose of this course is to present fundamental mathematical concepts in matrix theory, essential for contemporary approaches in data analysis, scientific computing, optimization, and virtually all quantitative domains in science and engineering. Topics covered include fundamental matrix operations, SVD, matrix factorizations, Jordan canonical form theorem, Hermitian matrices, algebraic and analytic properties of norms, duality and geometric properties of norms, vector norms, and condition numbers for inverse and linear system. Although the selection of topics is influenced by their application in diverse fields, the course will prioritize exploring the theoretical and conceptual foundations of this subject, aligning with the approach taken in other applied mathematics courses.
Contact the department for specific course information
A graduate level introduction to numerical algorithms for linear algebra problems with applications to data analytics. Algorithms will be studied and analyzed for efficiency and accuracy. Topics include Singular Value Decomposition, QR factorization, Least Squares, Conditioning and Stability, Systems of Equations, Eigenvalues and Eigenvalue algorithms and Iterative methods.
The course covers theoretical foundations necessary for the in-depth understanding of modern optimization methods for data science. The optimization methods are presented in the context of relevant applications. The course prepares students for applications of modern numerical methods to problems in data science and helps them build sufficient mastery of optimization tools and techniques for designing and implementing tailored methods for solving new problems.
This course presents a graduate level comprehensive introduction to data visualization and technical reporting. The course will provide the students with the necessary background for visual representation and analytics of complex data and data communication to a target audience. The course will cover design strategies, techniques to display multidimensional information structures, and exploratory visualization tools. As part of the course, students will be required to present written reports and oral presentations.
A graduate level introduction to new techniques for predictive descriptive learning using concepts from statistics, programming and artificial intelligence with emphasis on statistical aspects and integration with standard methodologies. Course covers regression and classification models with descriptive methods to discover patterns and data relationships without inference. This course will prepare students to view data from a statistical perspective with automated analysis of large complex data sets.
The course structure follows a graduate case-study model. Throughout the semester, students will be presented with various case studies of mathematical models as applied to the fields of engineering, technology, natural/physical science, social science, business, and/or management. Completion of a formal project with proposal describing the modeling problem with outline of a possible solution path concluding in guided solution as primary focus. Regular progress reports and presentation of the completed project by the end of the semester will be required. The project will provide solution(s) to the modeling problem and demonstrate skill on problem-solving, data-fitting, writing, and presenting.
A graduate level of statistical methods with emphasis towards applications in data sciences. Statistical learning methods regularly provide the foundations for data science, the methodologies included in this course will provide the students the knowledge needed in several fields as marketing, finance, and other disciplines. This course will prepare the students for modeling and understanding the fundamentals of statistical methods useful for modeling, analyzing and forecasting problems, which include big data.
A graduate level introduction to issues of ethical deliberation involved data analytics including topics like machine learning and working with incomplete data. Issues on how to collect data to reflect population of interest, model validations with appropriate error rate, model performance to standards when deployed are explored. Choice of learning algorithm and approach to maximize models' performance with interpretability with consideration of ethics into trade-off considerations are studied. D ecision making for real-world effects. Reporting and communication topics emphasized through projects.
The course structure follows a graduate case-study model. Throughout the semester, students will be presented with various case studies of mathematical models as applied to the fields of engineering, technology, natural/physical science, social science, business, and/or management. Completion of a formal project with proposal describing the modeling problem with outline of a possible solution path concluding in guided solution as primary focus. Regular progress reports and presentation of the completed project by the end of the semester will be required. The project will provide solution(s) to the modeling problem and demonstrate skill on problem-solving, data-fitting, writing, and presenting.
Source: Norfolk State University's catalog, linked per course · table learning_unit · CourseShelf publish 59
