Support for students who are concurrently enrolled in MATH 90, Precalculus Algebra. Topics include concepts and skills from elementary and intermediate algebra that are needed to understand the basics of precalculus algebra. Concepts are taught in the context of the linked Math 90 course.
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.
Support for students who are concurrently enrolled in MATH 80, Probability and Statistics. Topics include concepts and skills from arithmetic, pre-algebra, elementary and intermediate algebra, and descriptive statistics that are needed to understand the basics of college-level statistics. Concepts are taught in the context of the linked Math 80 course.
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.
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.
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.
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.
Survey of mathematics for students with nontechnical goals. Topics include problem solving, set theory, logic, number theory, modeling with functions, geometry, finance, combinatorics, probability, and the role of mathematics in modern society. This course is designed to enhance student appreciation of both the beauty and utility of mathematics.
Survey of mathematics for students with nontechnical goals. Topics include problem solving, set theory, logic, number theory, modeling with functions, geometry, finance, combinatorics, probability, and the role of mathematics in modern society. This course is designed to enhance student appreciation of both the beauty and utility of mathematics.
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.