Pre-Algebra students will strengthen their skills related to calculations and applications involving whole numbers, integers, fractions, decimals, and percentages. Additional topics include order of operations, algebraic expressions, solutions of equations, ratios, proportions, perimeter, and area. This course is part of the developmental math sequence at Snow College and is designed to prepare students for more rigorous college-level math courses.

Beginning Algebra students will study the real number system, order of operations, exponents, linear equations and inequalities in one and two variables, application problems, polynomials, factoring, and radicals. Math 0800 is part of the developmental math sequence at Snow College and is designed to prepare students for more rigorous college-level math courses.

This course prepares students to take GE math courses (MATH 1030 or MATH 1040) or to take the prerequisites required for more advanced math courses. A graphing calculator and internet access are required. Students are taught to use technology and other mathematical tools (such as algebra, geometry, and statistics) that will help them understand and analyze real-world data with more confidence. Students will develop, implement and analyze mathematical models to understand a variety of authentic and personally relevant situations relating to basic personal finance, investment, and business management just to name a few.

This course is part of Snow College’s math placement process; for students who desire to review or learn math topics in order to improve placement level before beginning a math course. This course addresses unique strengths and weaknesses of students from various backgrounds by providing each student with an individual assessment and study plan for mastering target material. This course requires mandatory class attendance and a minimum number of hours per week logged into a preparation module. May be repeated up to 4 times for credit. May be graded credit/no credit.

Intermediate Algebra students will study properties of the real number system including the use of set and/or interval notation and performing operations on real numbers. Students will continue the use of variables and the simplifying and evaluating of algebraic expressions. Solving and graphing of linear and quadratic equations along with an introduction to linear, quadratic, exponential, and logarithmic functions will be covered. Math 1010 is part of the developmental math sequence at Snow College and is designed to prepare students for more rigorous college-level math courses.

Quantitative Literacy is about viewing Mathematics from a countable, predictable, and creative standpoint. We learn how and where we find geometry, pattern, logic, cryptography and statistics in our natural world and in society. The discoveries are made using a little bit of algebra, art, trigonometry and other skills to critically process the concepts in the course. This course is designed for students seeking an AA or non-stem AS degree. Math 1030 is not a prerequisite for Math 1040, 1050 or 1060.

Introduction to Statistics is a first-semester course on the nature of statistical reasoning. Topics to be covered include descriptive statistics, sampling and data collection, basic probability, sampling distributions, and statistical inference (including 1- and 2-sample confidence intervals and hypothesis testing). Statistical calculator required (TI-84 recommended).

Introduction to Statistics (Extended) is a first-semester course on the nature of statistical reasoning. Topics to be covered include descriptive statistics, sampling and data collection, basic probability, sampling distributions, and statistical inference (including 1- and 2-sample confidence intervals and hypothesis testing). Statistical calculator required (TI-84 recommended). Math 1045 differs from Math 1040 by adding just-in-time content (algebra, etc.) in the extra time allotted.

College Algebra is designed to prepare students for trigonometry and calculus. In this course students will study several types of functions including polynomial, rational, exponential, and logarithmic functions. Additional topics may include graphing technology, sequences and series, conic sections, matrices, modeling, and the binomial theorem.

College Algebra is designed to prepare students for trigonometry and calculus. This course presents the first half of the content associated with college algebra. Specifically, the course focuses on functions, including polynomial, rational, exponential, and logarithmic equations. Students taking Math 1051 should plan to take Math 1052 upon successful completion of Math 1051. Math 1051 combined together with Math 1052 is the equivalent of a traditional Math 1050 course.

College Algebra is designed to prepare students for trigonometry and calculus. This course presents the second half of the content associated with college algebra. Specifically, the course focuses on systems of equations, vectors and matrices sequences and series. Additional topics may include analytical geometry, modeling, and the binomial theorem.

This course will cover trigonometric functions, definitions, radian/angle measure, graphs, solving trigonometric equations, identities, vectors, Law of Sines, Law of Cosines, complex numbers, polar coordinates. Graphing calculator required.

In this course students will study polynomial, rational, exponential, logarithmic, and trigonometric functions, relations, and applications; additional topics include sequences and series, conic sections, matrices, the binomial theorem, modeling, and graphing technology. This course prepares students for calculus.

Applied Calculus introduces the techniques of elementary calculus for functions of one variable, including differentiation and integration. Applications are emphasized in the areas of biological, management and social sciences. Techniques of calculus of several variables, including partial differentiation and multiple integrals, are introduced.

Students will learn about the interaction between statistical and mathematical reasoning and their application to the collection, preparation, and presentation of data. In addition to traditional structured data analysis, this course will also consider unstructured data such as natural language and image processing. Access to a computer is required. This course fulfills the Math GE requirement. The course will also serve as a prerequisite to later data science courses, i.e., Math 2080/3080. The course is designed to support students interested in pursuing data heavy degrees/careers.

This course is an introduction to calculus: functions and their limits, especially as applied to derivatives and integrals. Topics include continuity of functions, techniques and applications of differentiation (related rates, graphing, and optimization), and elementary techniques and applications of integration. These topics are applied to algebraic, trigonometric, exponential, and logarithmic functions.

This course is a continuation of the study of calculus. Topics include techniques of integration and applications, numeric integration techniques, calculus in conic sections and polar coordinates, infinite sequences and series (tests for convergence), and introduction to vectors.

Algebraic Reasoning with modeling presents the basic ideas of sets and functions in the context of and motivated by modeling bivariate data. Some basic concepts of the course include the concept of basic set theory such as unions, intersections, Venn diagrams, etc. The course also addresses basic ideas and algebra of functions, including polynomial, exponential, and logarithmic functions. Other topics include basic combinatorics, counting principles, and arithmetic and geometric sequences. The course culminates in a pictorial introduction to the basic ideas of calculus presented with minimal computation.

Mathematics for Elementary Teachers I is part of a series of courses designed to improve the mathematical understanding of prospective elementary teachers. Concepts covered include problem-solving, sets, functions, numeration systems, number theory, rational numbers (fractions), decimals, percents, and integers. The course will combine a thorough treatment of mathematical concepts with pedagogical philosophy to help prospective teachers learn to teach mathematics with understanding and insight.

Mathematics for Elementary Teachers II is part of a series of courses designed to improve the mathematical understanding of prospective elementary teachers. Concepts covered include basic statistics, probability, properties of geometric shapes, measurement using English and Metric systems, geometry using triangle congruence (including constructions), and geometry using transformations. The course will combine a thorough treatment of mathematical concepts with pedagogical philosophy to help prospective teachers learn to teach mathematics with understanding and insight.

Applied Statistics is the study of the nature of statistical reasoning and includes topics such as descriptive statistics, sampling and data collection, probability, hypothesis testing including Chi Square and Analysis of Variance, correlation, and regression. This course is primarily for business and mathematics or statistics majors. Graphing calculator required (TI-83/84 preferred).

Students will get an introduction to Python programming, data analysis tools, and the necessary statistics to acquire, clean, analyze, explore, and visualize data using real-life data sets. Using statistics, students will learn to make data-driven inferences and decisions, and to communicate those results effectively. This course is designed for students outside of engineering and the sciences. Students with majors in engineering or science should take Math 3080 instead.

This course is a continuation of the study of calculus. Topics include vectors in two and three-dimensional space, quadric surfaces, cylindrical and spherical coordinates, calculus of vector-valued functions, partial derivatives and the gradient, limits and continuity of functions of several variables, vector fields and line integrals, multiple integrals, Green's, Stoke's, and Divergence Theorems.

This course explores methods of solving ordinary differential equations which describe much of the physical phenomena in our world. Linear algebra topics will include systems of linear equations, matrix operations, vector spaces, and eigensystems. The course examines techniques for solving linear and nonlinear first-order differential equations as well as higher-order linear equations. Other topics will include initial-value problems, Laplace transforms, numerical methods, and modeling. The course is designed for students with majors in specific engineering and science disciplines. Students with majors in other science and engineering disciplines, and students with a mathematics major should take MATH 2270 and MATH 2280 instead.

Linear algebra is a study of systems of linear equations, matrices, vectors and vector spaces, linear transformations, eigenvalues and eigenvectors, and inner product spaces. This class is required for students majoring in mathematics and many areas of science and engineering.

This is a course which covers methods of solving ordinary differential equations. The class is designed to meet the needs of math, engineering, and certain science majors. Included in the class are techniques for finding solutions to linear and nonlinear first-order differential equations as well as higher-order linear equations with constant and variable coefficients. Laplace transforms, power series solutions, and several numerical approximation methods are also addressed. Some mathematical modeling of differential equations is included.

This is a first course in statistics for STEM majors. Topics will include graphing techniques, probability theory, discrete and continuous distributions, descriptive statistics, and statistical inference (confidence intervals and hypothesis testing, including linear regression and one-way ANOVA). Proficiency with integral calculus is required.

Students will get an introduction to Python programming, data analysis tools, and the necessary statistics to acquire, clean, analyze, explore, and visualize data real-life data sets. Using statistics, students will learn to make data-driven inferences and decisions, and to communicate those results effectively.

sStudents will learn to efficiently find structures and patterns in large data sets. Topics will include acquiring data sets and cleaning messy and noisy raw data sets into structured and abstract forms; applying scalable and probabilistic algorithms to these well-structured abstract data sets; and, formally modeling and analyzing the error inherent in these methods. Students will consider data representations and trade-offs between accuracy and scalability.

This course in discrete mathematics covers Boolean algebra, logic and proof, sets and relations, functions, induction, recursion, enumerative combinatorics, elements of number theory, and graph theory.

This course introduces the theory and application of machine learning, sometimes referred to as artificial intelligence. Students who take this course will understand and be able to deploy basic supervised and unsupervised learning techniques including” decision trees, neural networks, kernel methods, support vector machines, and probabilistic methods. The course will be taught using Python, R, Matlab, or a similar programming language.