IGETC Area 2

An intensive preparation for calculus. This course is intended for computer science, engineering, mathematics and natural science majors. Topics include algebraic, exponential, logarithmic and trigonometric functions and their inverses and identities, conic sections, sequences, series, the binomial theorem and mathematical induction.

This course is intended for students majoring in Science, Technology, Engineering, or Mathematics (STEM). Math 4 in combination with Math 3 (Trigonometry with applications) serves as a prerequisite for Math 7 (Calculus 1). The topics to be covered include review of the fundamentals of algebra, relations, functions, solutions of first and second degree equations and inequalities, systems of equations, matrices, binomial theorem, mathematical induction, polynomial and rational functions, exponential and logarithmic functions, analytic geometry and conic sections, and geometric and arithmetic sequences and series.

This first course in calculus is intended primarily for science, technology, engineering and mathematics majors. Topics include limits, continuity, and derivatives and integrals of algebraic and trigonometric functions, with mathematical and physical applications.

This second course in calculus is intended primarily for science, technology, engineering, and mathematics majors. Topics include derivatives and integrals of transcendental functions with mathematical and physical applications, indeterminate forms and improper integrals, infinite sequences and series, and curves, including conic sections, described by parametric equations and polar coordinates.

This course is intended for computer science, engineering, and mathematics majors. Topics include proof techniques, cardinality of sets, partial orderings and equivalence relations, symbolic logic and valid arguments, permutations and combinations with repetition, and an introduction to graph theory.

Topics include vectors and analytic geometry in two and three dimensions, vector functions with applications, partial derivatives, extrema, Lagrange Multipliers, multiple integrals with applications, vector fields. Green's Theorem, the Divergence Theorem, and Stokes' Theorem.

Topics include matrices and linear transformations, abstract vector spaces and subspaces, linear independence and bases, determinants, systems of linear equations, eigenvalues and eigenvectors.

This course is an introduction to ordinary differential equations. Topics include first order equations, linear equations, reduction of order, variation of parameters, spring motion and other applications, Cauchy-Euler equations, power series solutions, Laplace transform, and systems of linear differential equations.

This is a terminal mathematics course for liberal arts and social science majors. Topics include sets and counting, probability, linear systems, linear programming, statistics, and mathematics of finance, with emphasis on applications.

This course is a preparatory course for students anticipating enrollment in Math 28 (Calculus I for Business and Social Science). It is not recommended as a terminal course to satisfy transfer requirements. Topics include algebraic, exponential and logarithmic functions and their graphical representations, and using these functions to model applications in business and social science.

This course is intended for students majoring in business or social sciences. It is a survey of differential and integral calculus with business and social science applications. Topics include limits, differential calculus of one variable, including exponential and logarithmic functions, introduction to integral calculus, and mathematics of finance.

Topics include techniques and applications of integration, improper integrals, functions of several variables, partial derivatives, method of least squares, maxima and minima of functions of several variables with and without constraints, method of LaGrange Multipliers, double integrals and their application, elementary differential equations with applications, probability and calculus.

This course is an introduction to statistical thinking and processes, including methods and concepts for discovery and decision-making using data. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-squared, and t-tests; and application of technology for statistical analysis including the interpretation of the relevance of the statistical findings. Students apply methods and processes to applications using data from a broad range of disciplines.