52221

This course explores collaborative and inclusive leadership and management styles along with the vision and values needed to effectively lead successful organizations. Examines strategies for developing and encouraging skills that enable leaders and managers to succeed in business, government, nonprofits, education, and community-based organizations.

Concepts and principles of human relations in organizations, including application of motivational theory, group dynamics, managing change, leadership styles, training, and organizational development.

Concepts of supervision and management including planning, organizing, recruiting, training, decision-making, communication, employee relations, motivation, discipline, rewards/incentives, and grievances.

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.

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.

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.

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.

This course emphasizes topics of relevance to mathematics and computer science majors: logic, proof techniques, mathematical induction, set theory, elementary number theory, functions and their growth, relations, recursion, combinatorics, analysis of algorithms, trees, and graphs.