41 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.
A non-credit, pre-college course in intermediate algebra stressing graphing and equation solving, algebraic manipulation, laws of exponents functions, and logarithms.
A non-credit, pre-college course in algebra and trigonometry, covering functions (including exponential, logarithmic and trigonometric functions), analytic trigonometry, linear algebra (including system of equations, matrices, and determinants) and analytic geometry. This course does not satisfy the core mathematics requirement.
A problem-solving oriented course for prospective elementary school teachers. Topics will include some of the following: properties of number systems, place value and scales of notation, elementary number theory, geometry and measurement, statistics probability. Open only to students in the Elementary Education program. Offered each Fall.
This course focuses on three main topics relating math and politics. Polling is a tool ubiquitous in politics. We will determine what makes a good poll, and how much information one can actually infer from a poll. From there we will study the many, and sometimes apparently contradictory, statistical claims made by candidates, ads, and pundits in order to advance their positions. By using mathematical reasoning we will determine what truth lies behind these claims. Finally, we will look at voting to see how much your vote actually counts. We will also compare our (US) voting systems to others around the world, and try to understand what makes a voting system "fair.".
We all need to make decisions. As citizens, we need to sift through the mountain of (often misleading) data that is constantly being thrown our way by advertisers, the media, politicians, etc. As professionals, we may need to make decisions using data from such diverse areas as economics, social policy, health care, the military, or the environment. In any role, we need to know how data can be turned into useful information. This course covers mathematics used to analyze data in order to make good, informed decisions. Major topics include informal logic, data interpretations, basic probability, introductory statistics, and economics.
A data-oriented, applied introduction to statistics. Topics include descriptive statistics, data distributions, random sampling, relationships, confidence intervals and hypothesis testing. Statistical software will be used throughout this course.
A voluntary course focusing on building a solid foundation of statistical knowledge, especially for those who need to take MTH 112, Introduction to Statistics II), as well, students' studying Business, Psychology, and various other fields. It will provide additional time to review prerequisite knowledge needed to successfully apply to various statistical analyses, improve on note-taking skills, and develop study strategies to prepare for tests using various methods. Technology will be incorporated to aid in calculations and visualizing data.
This course is a continuation of MTH 110. Further methods of statistics and their use in life will be covered. It includes: inference for one and two population means, inference for two proportions and two variances, inference for simple and multiple regression, categorical data analysis, analysis of variance, nonparametric tests and logistic regression. A statistical program will be used throughout this course.
This course includes the following topics: exponential functions and models, mathematics of finance, linear systems and matrices, linear programming, derivatives. There is particular emphasis on applied problems. Students may not take both MTH 120 and MTH 123.
Elementary functions, exponential and logarithmic functions, continuity, derivatives, max-min methods and applications. Primarily for students in economics and accounting.
An introduction to various contemporary applications of mathematics drawn from the following topics: linear algebra, combinatorics, graph theory, probability, modern algebra. The emphasis is on the interplay between theory and application in mathematics. Students may not take both MTH 120 and MTH 123.
A study of differential and integral calculus of one variable and applications.
This course will focus on material that is foundational to success in MTH 145. It will cover topics including: simplifying rational and algebraic expressions, solving linear equations and inequalities, functions and function operations, exponential and logarithmic functions and equations, and trigonometric functions and equations. Students will see how these topics are used in MTH 145 problems and gain time to practice these problems. Additionally, students will develop their problem solving skills, collaborate with their peers, and learn study strategies that will help them prepare for Calculus I assessments. Permission of Department Chair required.
A study of differential and integral calculus of one and several variables and applications. Differential equations and their solutions.
This course covers certain advanced topics used for planning, executing, and evaluating statistical studies based on experiments, surveys, and observational datasets. The goals of this course are to further statistical literacy, to deepen understanding of certain advanced statistical principles that are not covered in the current curriculum, to learn to use technology to analyze data and aid in conceptual understanding, and to learn to use statistics to help understand and solve real-world problems. Prerequisite(s): MTH 110 and MTH 112.
This course will introduce students to the challenges of working with real data. Students will collaborate with faculty or other students on real research projects, and act as a the main data analyst for a real scientific project. The course will cover how to meet with clients, how to write reports, how to clean and manage data, and how to present results. Most particularly, the course will cover how best to analyze a data set based on the research questions of interest, and will teach the students the skills they need to run these analyses.
This course provides an understanding of the fundamental concepts of financial mathematics from an actuarial perspective, and how those concepts are applied in calculating present and accumulated values for various streams of cash flows as a basis for future use in: reserving, valuation, pricing, asset/liability management, investment income, capital budgeting, and valuing contingent cash flows. The course will cover topics of CAS Exam 2/SOA Exam FM. Note: Mandatory for students whose concentration is actuarial science to take this course and pass with at least a grade of B-.
Multi-variate calculus with vectors. Line integrals and Green's theorem.
This course covers the fundamental mathematical principles relevant to computer science, applied mathematics, and engineering. Topics included are propositional logic, predicate logic, proof techniques, (with an emphasis on mathematical induction), basics of counting and discrete probability.
Systems of linear equations, matrix algebra, vectors and vector spaces, linear transformations, inner product spaces, determinants, characteristic values and vectors. (Offered each Fall) Students will not be able to get credit for both MTH 261 and MTH 304.
Differential equations play a vital role in modeling nearly every physical, chemical, and biological process. Understanding how to create and interpret mathematical models, as well as how to solve and characterize solutions of differential equations, is of fundamental importance to applied mathematics, contemporary science, and engineering. This course provides a first introduction to this ubiquitous field of analysis with a heavy emphasis on the development, refinement, and interpretation of mathematical models using differential equations. The first portion of the course examines the various analytical, qualitative and numerical techniques available for analyzing the solutions of linear and nonlinear first-order differential equations (topics include separable equations, linear equations, transformation of variables, series methods, integral transforms, slope fields, equilibria, Euler's Method, and bifurcations). The second portion of the course focuses on techniques for studying systems of first-order differential equations (topics include direction fields, phase planes, Euler's Method for systems, homogeneous and nonhomogeneous linear systems, and the linearization of nonlinear systems). Students will not be able to get credit for both MTH 303 and MTH 304.
Primarily intended for physics and pre-engineering majors, this course integrates the study of linear algebra and differential equations with substantial emphasis placed on understanding the deep connections between these foundational fields of study. Linear algebra topics covered in this course include Gauss-Jordan elimination, matrix algebra, inverse matrices, determinants, eigenvalues/eigenvectors, linear transformations, vector spaces, linear independence, span, basis and dimension. Differential equations topics covered in this course include separable equations, linear equations, transformation of variables, series methods, integral transforms, slope fields, Euler's Method, homogeneous and nonhomogeneous linear systems, and basic solution techniques for solving partial differential equations. Students will not be able to get credit for both MTH 261 and MTH 304, or for both MTH 303 and MTH 304.
Some classical puzzles of recreational mathematics; enumeration techniques; combinatorial designs; graph theory and network flows.
This course will explore the mathematics of encryption and decryption from the first ciphers to encryption methods used in modern internet security. Students will also be expected to write their own encryption and decryption protocols in Python.
Basic probability theory, combinatorial analysis, independence and dependence. Discrete and continuous distributions, random variables, random vectors, multivariate distributions. Expectations and moment generating functions. Binomial, normal, Poisson and related distributions. Sums and sequences of random variables. Central limit theorem. (Offered each fall.).
Distributions related to the normal. Estimation: consistency, unbiasedness, mean square error, sufficiency, method of moments, maximum likelihood estimates, confidence limits and intervals. Bayesian intervals. Tests of hypotheses: tests of simple hypotheses, Neyman-Pearson lemma, Bayes procedures, composite hypotheses, generalized likelihood ratio tests. Non-parametric procedures. Simple linear regression. (Offered every other spring.).
Review of statistical methods. Simple and multiple linear regression. Regression diagnostics. Time series models. Moving average, autoregressive and ARIMA models. Forecasting with regression and time series models. (Offered every other spring.).
This is a course designed to prepare students to pass the actuary exam P. This course covers actuarial applications of set theory, combinatorial probability, Bayes theorem, probability density functions, joint probability functions, and marginal and conditional probability. It also introduces several advanced topics, including transformations, order statistics, and a number of named distributions not covered in MTH 311.
This proposed 3-credit course will cover the usual topics in an introductory-level course in modern Data Science, including Statistical Learning. More specifically, the topics that will be covered are: Linear models (Validation, Resampling Methods, Model Selection and Regularization, Shrinkage, Dimension Reduction). Generalized Linear Models (Logistic and Probit Regression Models, Categorical and Count Response, Measures of Fit) Unsupervised Learning (Decision trees and Random Forests, Bootstrap, Bagging, Principal Components, Cluster Analysis)
An introduction to techniques of mathematical proof, with emphasis on the recognition and evalution of problem structures common to all areas of mathematics. Application of these techniques to a detailed description of the Real Number system. Examination of the algebraic, set-theoretic and topological structures of the Real Number system (completeness, compactness, connectedness) and real sequences. (Offered each spring.).
The complex number system. Differentiability and the Cauchy-Riemann conditions. The exponential, trigonometric and logarithm functions. Complex integration; the Cauchy integral theorem and its implications. Consequences of the Cauchy integral formula. Taylor and Laurent series, singularities.
Introduction to group theory. Cyclic, Abelian, symmetric and product groups. Subgroups, equivalence relations, homomorphisms. (Offered each fall.).
Topics from the foundations of geometry, non-Euclidean geometry, transformation theory, dissection theory, convexity. (Offered every spring.).
A working introduction to general heuristic reasoning (including specialization, generalization, analogy and induction) useful in solving mathematical problems. (Offered every spring.).
A student who wishes to pursue an independent study project for academic credit must submit, prior to registration, a proposed plan of study that includes the topic to be studied and goal to be achieved, the methodology to be followed, schedule of supervision, end product, evaluation procedure and number of credits sought. The proposal must be approved by the supervising faculty member, the department chair and the dean of arts and sciences.
The development of algorithms for and error analysis of: solutions of equations, interpolation and approximation, numerical differentiation and integration, numerical solutions of differential equations.
Topological spaces; separation and countability properties. Mappings and continuity. Compactness and connectedness of various types. Product and quotient spaces.
A limited number of internships are available to students to provide them with practical experience in the applications of mathematics in business.
This course is designed solely for Mathematics majors with an aim of making students understand, annotate and communicate (both verbally and in writeen form) the contents of a mathematical/statistical article. Also discussed will be the basics of the scientific document preparation system LaTeX.
This course, exclusively for senior mathematics majors, involves the completion and presentation of a research project in the student's area of concentration. Prior to registration for the course, a student must submit a proposal and have it approved by the department chair. Students may work in teams of two or three on projects; however, team projects will require commensurably greater scope than individual projects.
Source: Le Moyne College's catalog, linked per course · table learning_unit · CourseShelf publish 61
