This course is designed to give students a better understanding of the concepts in computer science by software design and programming using Python. It will focus on how to think algorithmically by solving problems using a variety of assignments. This course is designed to help students become a better problem solver and gain a better perspective about creative computing.
For nonmajors. Selected topics illustrating, mathematics as a way of representing and, understanding patterns and structures, as an art,, as an enabler in other disciplines, and as a, historical force. Emphasis changes from semester, to semester, reflecting the expertise and, interests of the faculty member teaching the, course. For further information consult the, appropriate faculty member before registration.
Data analysis, data production, statistical, inference. Data analysis: methods and ideas for, organizing and describing data using graphs,, numerical summaries, and other statistical, descriptions. Data production: methods for, selecting samples and designing experiments to, produce data that can give clear answers to, specific questions. Statistical inference:, methods for moving beyond the data to draw, conclusions about some wider universe. Credit may, not be earned for both this course and AP, statistics.
The basic functions encountered in calculus,, discrete mathematics, and computer science:, polynomial, rational, exponential, logarithmic,, and trigonometric functions and their inverses., Graphs of these functions, their use in problem, solving, their analytical properties. May not be, taken for credit if AP Calculus credit has been, granted.
Basic techniques of abstract formal reasoning and, representation used in the mathematical sciences., First order logic, elementary set theory, proof by, induction and other techniques, enumeration,, relations and functions, graphs, recurrence, relations.
Basic skills and concepts that evolve from the, study of systems of linear equations. Systems of, linear equations, Euclidean vector spaces and, function spaces, linear transformations, matrices, and determinants, inner product spaces, eigenvalue, problems, symmetric transformations.
Introduction to combinatorial theory, including, one or more of the following: enumeration,, algebraic enumeration, optimization, graph theory,, coding theory, design theory, finite geometries,, Latin squares, posets, lattices, Polya counting,, Ramsey theory.