47666

First course in a three-semester calculus sequence, this course covers differential calculus through the study of limits, continuity, differentiation, applications of differentiation, and an introduction to integration.

First course in a three-semester calculus sequence, this course covers differential calculus through the study of limits, continuity, differentiation, applications of differentiation, and an introduction to integration.

First course in a three-semester calculus sequence, this course covers differential calculus through the study of limits, continuity, differentiation, applications of differentiation, and an introduction to integration.

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.

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.

MATH 100A is the first course in a two-semester sequence in applied calculus. Lines, algebraic functions, exponential functions, logarithmic functions, limits, derivatives, and integrals, with applications.

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.

First course in a three-semester calculus sequence, this course covers differential calculus through the study of limits, continuity, differentiation, applications of differentiation, and an introduction to integration.

Trigonometric functions and their graphs; trigonometric identities and equations; inverse trigonometric functions; solving triangles; complex numbers.

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.