Real functions and their graphs; one-to-one and inverse functions; polynomial, rational, exponential and logarithmic functions; complex numbers and zeros of polynomials; linear systems and matrices; geometric transformations and conic sections; topics in discrete mathematics.
Real vector spaces, subspaces, linear dependence and span, matrix algebra and determinants, basis and dimension, inner product spaces, linear transformations, eigenvalues and eigenvectors, proofs of basic results.
Support for students who are concurrently enrolled in MATH 110A, Calculus I. Topics include concepts and skills from precalculus and trigonometry that are needed to understand the basics of Calculus I. Concepts are taught in the context of the linked Math 110A course.
Support for students who are concurrently enrolled in MATH 75, Mathematical Analysis for Business. Topics include concepts and skills from elementary and intermediate algebra that are needed to understand the basics of Mathematical Analysis for Business. Concepts are taught in the context of the linked Math 75 course.
Complete both precalculus algebra and trigonometry by taking this single class.? Topics covered include real functions and their graphs; one-to-one and inverse functions; algebraic, exponential and logarithmic, and trigonometric functions; complex numbers and zeros of polynomials; matrices; transformations and conic sections; discrete mathematics; polar coordinates; and applications of trigonometric identities.
Descriptive statistics: organization of data, sample surveys, experiments and observational studies, measures of central tendency and dispersion, correlation, regression lines, and analysis of variance (ANOVA). Probability theory. Random variables: expected value, variance, independence, probability distributions, normal approximation. Sampling: sampling distributions, and statistical inference, estimating population parameters, interval estimation, standard tests of hypotheses.
Support for students who are concurrently enrolled in MATH 75, Mathematical Analysis for Business. Topics include concepts and skills from elementary and intermediate algebra that are needed to understand the basics of Mathematical Analysis for Business. Concepts are taught in the context of the linked Math 75 course.
Topics include real vector spaces, subspaces, linear dependence, span, matrix algebra, determinants, basis, dimension, inner product spaces, linear transformations, eigenvalues, eigenvectors, and proofs. Ordinary differential equations and first-order linear systems of differential equations; explicit solutions; qualitative analysis of solution behavior; linear structure, existence, and uniqueness of solutions. Partial differential equations.
Foundations of Data Science combines an introductory look into the fundamental skills and concepts of computer programming and inferential statistics with hands-on experience in analyzing datasets by using common tools within the industry. Additionally, the course investigates ethical issues surrounding Data Science, such as data privacy.
Descriptive statistics: organization of data, sample surveys, experiments and observational studies, measures of central tendency and dispersion, correlation, regression lines, and analysis of variance (ANOVA). Probability theory. Random variables: expected value, variance, independence, probability distributions, normal approximation. Sampling: sampling distributions, and statistical inference, estimating population parameters, interval estimation, standard tests of hypotheses.