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Data types, evaluation of expressions, control statements, functions and subroutines, interactive and file I/O. Program development, documentation, and testing. Problem analysis and algorithm design. Applications to numeric problems and character-processing. 4 lectures/problem-solving. Prerequisites: MAT 105 and MAT 106 with grades of C or better, or consent of instructor.

Basic concepts of computer software and programming. Data types, expressions, control structures, functions, file and stream I/O. Use of pointers and dynamic storage allocation. Structured and abstract data types. Problem-solving techniques. 4 lectures/problem-solving. Prerequisites: MAT 105 and 106 with grade of C or better, or consent of instructor. Cannot be used for CS elective credit.

Sequences and Series, L’Hospital’s Rule, Polar Coordinates, Improper Integrals, Parametric Equations and Conic Sections. 4 lectures/problem-solving. Prerequisite: C or better MAT 115 or consent of the instructor.

Numerical methods of topics from algebra and calculus. Topics will include function evaluation and graphing, limits, summation, solving nonlinear equations, numerical integration and differentiation and an introduction to numerical error. 4 lectures/problem-solving. Prerequisites: C or better in MAT 116 and CS 128 or consent of instructor.

Introduction to linear transformations of the plane, vector space of n-tuples, matrix algebra, determinants, systems of linear equations. 4 lectures/problem-solving.  Prerequisite: C or better in MAT 214 or consent of instructor.

Introduction to vectors, dot products, cross products, equations of lines and planes. Calculus of Vector Valued Functions including unit tangents, unit normals and curvature. Introduction to multivariable functions, the Differential Calculus of Multivariable Functions, the chain rule, applications including extreme problems and Lagrange multipliers. 3 lectures/problem-solving. Prerequisite: C or better in MAT 116 or consent of instructor.

Integral Calculus of Multivariable functions, double and triple Integrals, applications of double and triple integrals, line and surface integrals, Green’s Theorem, Divergence Theorem, Stokes Theorem. 3 lectures/problem-solving. Prerequisite: C or better in MAT 214 or consent of instructor.

The theory of ordinary differential equations with emphasis on the linear case. 4 lectures/problem-solving. Prerequisite: C or better in MAT 116 or consent of instructor.

Basic set theory and logic, relations, functions, mathematical induction, countable and uncountable sets. Emphasis on how to present and understand mathematical proof. 4 lectures/problem-solving. Prerequisite: C or better in MAT 116 or consent of instructor.

Metric spaces and continuity. Analysis of functions of a single variable. Sequences, limits, continuity, differentiation, integration, introduction to function spaces. 4 lectures/problem-solving. Prerequisite for MAT 314: C or better in MAT 215 and MAT 310 or consent of instructor. Prerequisite for MAT 315: C or better in MAT 314 or consent of instructor.

Introduction to algebraic structures; groups, rings, integral domains, fields; mappings with emphasis on morphisms. 4 lecture/problem-solving. Prerequisite for MAT 417: C or better in MAT 310 or consent of instructor. Prerequisite for MAT 418: C or better in MAT 417 or consent of instructor.

Algebra and geometry of complex numbers; analyticity, mappings of elementary functions; Cauchy integral formula, Taylor and Laurent series, the residue theorem; conformal mapping with applications. 4 lecture/problem-solving. Prerequisite for MAT 428: C or better in MAT 314 or consent of instructor.  Prerequisite for MAT 429: C or better in MAT 428 or consent of instructor.

Rules of probability, random variables, expected values of random variables, distribution of functions of a random variable. Discrete and continuous probability distributions with applications. Descriptive statistics, central limit theorem and estimation. 4 lectures/problem-solving. Prerequisite: C or better in MAT 116 or Mat 131 or consent of instructor. Not open to students with credit in STA 315 or ECE 315.

Joint distribution, central limit theorem. Maximum likelihood estimation. Point and interval estimation, hypothesis testing. Small and large sample inferences. Contingency table analysis and Chi-square tests. Linear regression and correlation.  Use of computer package for applied problems.  4 lectures/problem-solving. Prerequisite: C or better in STA 241 or consent of instructor.