University of New Hampshire 1 at least through multivariate calculus, and must have knowledge of basic MATHEMATICS AND statistics and basic linear algebra at the undergraduate level. STATISTICS (MATH) Applicants for the degree of master of science for teachers (M.S.T.) usually possess a background equivalent to at least a minor in mathematics and must have either: completed education courses Degrees Offered: Ph.D., M.S., M.S.T., sufficient for certification, have three years teaching experience, or Graduate Certificate currently hold a full-time teaching position. This program is offered in Durham. https://ceps.unh.edu/mathematics-statistics The mission of the Mathematics and Statistics program is twofold: to prepare students for a variety of exciting and rewarding career Programs opportunities in business, industry, government and the teaching professions; and to advance forefront knowledge in the areas of pure • Mathematics (Ph.D.) mathematics, applied mathematics, statistics, and mathematics • Mathematics Education (Ph.D.) education through world-class cutting-edge research. • Statistics (Ph.D.) The Department of Mathematics and Statistics offers programs • Mathematics (M.S.) leading to a master of science for teachers (M.S.T.) in mathematics, • Mathematics: Applied Mathematics (M.S.) master of science in mathematics, master of science in mathematics • Mathematics (M.S.T.) with an option in applied mathematics, and a master of science in • Statistics (M.S.) statistics. Students in the master of science in applied mathematics may choose approved courses in the doctoral program in Integrated Applied • Industrial Statistics (Graduate Certificate) Mathematics as part of their MS program. The department also offers doctor of philosophy programs in Courses mathematics, integrated applied mathematics, statistics, and mathematics education. Mathematics & Statistics (MATH) In general, the master's degree programs offer the student a high level MATH 801 - Exploring Mathematics for Teachers I of preparation for professional employment as well as appropriate Credits: 3 preparation for programs leading to the Ph.D. The Ph.D. programs prepare Provides prospective elementary teachers with the opportunity to the student primarily for a career in university teaching and research. explore and master concepts involving number systems and operations, data analysis and probability. Additional topics may include geometry, The graduate programs have limited enrollment, allowing students to measurement, and algebraic thinking. Mathematical reasoning, problem work closely with faculty members in their areas of expertise. Research solving, and the use of appropriate manipulatives and technology are within the department is currently being conducted in many areas of integrated throughout the course. Readings, class discussions, and the mathematical sciences, including: operator theory, Hilbert spaces, assignments focus on mathematics content as well as applicable geometric function theory, complex analysis, ring theory, commutative theories of learning, curriculum resources, and state and national algebra, homological algebra, quantum groups, tensor categories, recommendations. The course models instructional techniques that can combinatorics, topology, algebraic topology, category theory, nonlinear be adapted to the elementary curricula. Credit offered only to M.Ed. and dynamics and chaos, data compression, chaotic prediction and control, M.A.T., certificate students, and in-service teachers. (Not offered for credit spectral analysis, asymptotic analysis, mathematical control theory, if credit is received for MATH #821 or MATH 823.) environmental statistics, spatial and spatio-temporal statistics, Bayesian Prerequisite(s): (EDUC 500 with a minimum grade of D- or EDUC 935 with and computational statistics, wavelets in statistics, teaching and a minimum grade of B-). learning of K-12 mathematics and statistics, teaching and learning of Equivalent(s): MATH 601, MATH #821, MATH 823 undergraduate mathematics and statistics, mathematical curriculum and Grade Mode: Letter Grade teacher education, and calculus learning. MATH #821 - Number Systems for Teachers Additionally, a graduate certificate in industrial statistics is offered. Credits: 3 Ways of representing numbers, relationships between numbers, number systems, the meanings of operations and how they relate to one another, Admission Requirements and computation with number systems as a foundation for algebra; Applicants for the M.S. and Ph.D. degrees in pure mathematics must episodes in history and development of the number system; and have completed significant undergraduate coursework in mathematics, examination of the developmental sequence and learning trajectory as preferably in algebra, analysis, and topology. children learn number concepts. Credit offered only to M.Ed., M.A..T., Elementary Math Specialist certificate only students, and in-service Applicants for the M.S. with applied mathematics option must have teachers. Not offered for credit if credit received for MATH 621. completed significant coursework in analysis or applied analysis. Equivalent(s): MATH 621 Applicants for the M.S. in statistics will typically have an undergraduate Grade Mode: Letter Grade degree in the mathematical, physical, biological, or social sciences or in engineering. Applicants must have completed mathematical coursework 2 Mathematics and Statistics (MATH) MATH 823 - Statistics and Probability for Teachers MATH 836 - Advanced Statistical Methods for Research Credits: 3 Credits: 3 An introduction to probability, descriptive statistics and data analysis; An introduction to multivariate statistical methods, including principal exploration of randomness, data representation and modeling. components, discriminant analysis, cluster analysis, factor analysis, Descriptive statistics will include measures of central tendency, multidimensional scaling, and MANOVA. Additional topics include dispersion, distributions and regression. Analysis of experiments generalized linear models, general additive models, depending on the requiring hypothesizing, experimental design and data gathering. Credit interests of class participants. This course completes a solid grounding offered only to M.Ed., M.A..T., Elementary Math Specialist certificate only in modern applications of statistics used in most research applications. students, and in-service teachers. Not offered for credit if credit received The use of statistical software, such as JMP, S PLUS, or R, is fully for MATH 623. integrated into the course. Prerequisite(s): (MATH 621 with a minimum grade of D- or MATH #821 Prerequisite(s): (MATH 835 with a minimum grade of B- or MATH 839 with a minimum grade of B-). with a minimum grade of B-). Equivalent(s): MATH 623 Grade Mode: Letter Grade Grade Mode: Letter Grade MATH 837 - Statistical Methods for Quality Improvement and Design MATH 831 - Mathematics for Geodesy Credits: 3 Credits: 3 Six Sigma is a popular, data-focused methodology used worldwide by A survey of topics from undergraduate mathematics designed for organizations to achieve continuous improvement of their existing graduate students in engineering and science interested in applications processes, products and services or to design new ones. This course to geodesy and Earth Sciences. Topics include essential elements from provides a thorough introduction to the Six Sigma principles, methods, analytic geometry, geometry of surfaces, linear algebra and statistics, and applications for continuous improvement (DMAIC process) and an Fourier analysis, discrete Fourier transforms and software, filtering overview of Design for Six Sigma (DFSS). Both manufacturing and non- applications to tidal data. manufacturing (transactional Six Sigma) applications will be included. Prerequisite(s): (MATH 645 with a minimum grade of D- or MATH 645H Emphasis is placed on the use of case studies to motivate the use of, as with a minimum grade of D- or MATH 762 with a minimum grade of D- or well as the proper application of, the Six Sigma methodology. Formal Six MATH 862 with a minimum grade of B-). Sigma Green Belt certification from UNH may be attained by successfully Grade Mode: Letter Grade completing TECH 696. Students must have completed a calculus-based MATH 832 - Introduction to the R Software introductory statistics course. Credits: 1 Grade Mode: Letter Grade This course provides a basic introduction to the open-sources statistical MATH 838 - Data Mining and Predictive Analytics software R for students who have never used this software or have never Credits: 3 formally learned the basics of it. Topics include: Numeric calculations, An introduction to supervised and unsupervised methods for exploring simple and advanced graphics, object management and work-flow, large data sets and developing predictive models. Unsupervised methods RStudio, user-contributed packages, basic programming, writing of include: market basket analysis, principal components, clustering, functions, statistical modeling and related graphs, distributed computing, and variables clustering. Important statistical and machine learning reproducible research and document production via markup language. methods (supervised learning) include: Classification and Regression Cr/F. Tress (CART), Random Forests, Neural Nets, Support Vector Machines, Equivalent(s): MATH 859 Logistic Regression and Penalized Regression. Additional topics focus Grade Mode: on metamodeling, validation strategies, bagging and boosting to improve MATH 835