# Course Offerings

- MATH–101 Mathematical Ideas (3 credits)
- Explores a variety of topics, including numeration systems, logic, geometry, probability, and statistics. Includes historical and cultural perspective and contemporary applications. Background should include two semesters of high school algebra. Fall, spring.
- MATH–105 College Algebra (3 credits)
- Treats properties of linear, quadratic, polynomial, exponential and logarithmic functions, inequalities, and systems of equations. Develops critical thinking and emphasizes real-world applications in the sciences and topical issues. Background should include three semesters of high school algebra. Fall, spring, summer.
- MATH–106 Precalculus Trigonometry (1 credit)
- Provides trigonometric tools necessary for success in Math 221. Develops trigonometric functions using both right triangles and the unit circle approach. Covers graphing, verification of identities, and inverse trigonometric functions. Requires no prior knowledge of trigonometry. Prerequisite: Grade of C or better in Mathematics 105 or an acceptable score on a placement exam. Fall, spring.
- MATH–134 Survey of Calculus (3 credits)
- Treats polynomial, exponential, and logarithmic functions, their derivatives and integrals. An introduction to the calculus of several variables and applications to the natural and social sciences. Recommended for students who plan to take only one semester of calculus not requiring trigonometry. Not open to mathematics majors or minors. Background should include four semesters of high school algebra and two semesters of geometry. Prerequisite: Grade of C or better in Mathematics 105 or an acceptable score on a placement exam. Does not satisfy the prerequisite for Mathematics 222. Credit not given for both Mathematics 134 and 221. Fall, spring.
- MATH–191 Special Topics in Finite Math (0.5-3 credits)
- Study of topics of special interest in finite (non-calculus based) mathematics. Treats material not covered in other courses. Topics will be announced. May be repeated. Background should include two semesters of high school algebra.
- MATH–202 Mathematics for Elementary Teachers (3 credits)
- Treats problem solving, the real number system, elementary number theory, geometry, and other topics. For elementary education majors only. Prerequisite: Mathematics 101. Spring.
- MATH–221 Calculus I (4 credits)
- Covers parametric and polar equations; limits and continuity; differentiation and integration of algebraic, trigonometric, logarithmic, and exponential functions; and applications of differentiation. Background should include eight semesters of high school mathematics, including four semesters of algebra, two semesters of geometry, and at least 12 weeks of trigonometry. Prerequisite: Grade of C or better in Mathematics 105 and permission of Department of Mathematics, or an acceptable score on a placement exam. Credit not given for both Mathematics 134 and 221. Fall, Spring, Summer.
- MATH–222 Calculus II (4 credits)
- Covers integration techniques and applications of integration. Introduces vectors and matrices, functions of several variables and their derivatives, differential equations, and multiple integrals. Prerequisite: Grade of C- or better in Mathematics 221. Fall, spring, summer.
- MATH–291 Special Topics in Calculus (0.5-3 credits)
- Study of aspects or applications of calculus not covered in the standard calculus sequences. Topics will be announced. May be repeated. Prerequisite: Mathematics 221.
- MATH–310 History of Mathematics (3 credits)
- Surveys the development of mathematics from the Ishango Bone to Newton, and Leibniz. Emphasizes major mathematical concepts, the cultural contexts in which they were discovered, and the solving of related mathematical problems. Prerequisite: Grade of C- or better in Mathematics 222.
- MATH–323 Calculus III (4 credits)
- Covers infinite series, vector-valued functions, multiple integration, line and surface integrals, and analysis of vector fields. Prerequisite: Grade of C- or better in Mathematics 222. Fall, spring, summer.
- MATH–324 Differential Equations (3 credits)
- Includes standard first- and second-order methods, systems, difference equations, power series, Laplace transforms, and numerical and nonlinear methods, with applications for all of these. Prerequisite: Grade of C- or better in Mathematics 222. Fall, spring, and summer.
- MATH–330 Theory of Interest (3 credits)
- Covers compound interest formulas, annuities, perpetuities, amortization schedules, bonds, and other securities. Provides preparation for the Society of Actuaries Exam FM. Prerequisite: Grade of C- or better in Mathematics 222. Fall 2017 (every other Fall).
- MATH–341 Linear Algebra (3 credits)
- Covers systems of linear equations, matrices, determinants, vector spaces, linear transformations, and eigenvalues and eigenvectors. Prerequisite: Grade of C- or better in Mathematics 222. Spring.
- MATH–355 Foundations of Geometry (3 credits)
- Develops from axioms various notions, including point, line, incidence, betweenness, congruence, parallelism, perpendicularity, distance, similarity, and perspective. Geometries include finite, Euclidean and hyperbolic, with emphasis on Euclidean constructions, proofs, transformations, and dynamic geometry using computer software. Prerequisite: Grade of C- or better in Mathematics 222 or consent of instructor. Fall 2011.
- MATH–365 Probability (3 credits)
- Develops standard topics in calculus-based axiomatic probability theory and applications, including permutations, combinations, sample spaces, events, random variable, independence, conditional probability, distributions, density functions, expected value, and moment generating functions. Prerequisite: Grade of C- or better in Mathematics 222. Fall.
- MATH–370 Discrete & Combinatorial Math (3 credits)
- Covers such topics as enumeration, principles of logic, set theory, mathematical induction, generating functions, recurrence relations, and graph theory. Prerequisite: Grade of C- or better in Mathematics 222. Fall.
- MATH–373 Numerical Methods (3 credits)
- Covers numerical computer-based methods for solving transcendental equations, systems of linear equations, interpolation, approximation, numerical integration and differentiation, and numerical solutions of ordinary differential equations. Prerequisite: Computer Science 205 or 210 or equivalent; Grade of C- or better in Mathematics 222. Mathematics 341 is suggested but not required. Spring 2019.
- MATH–391 Special Topics in Intermediate Math (0.5-3 credits)
- Covers topics not included in other courses to give greater depth in certain areas and to explore current mathematics topics. Topics vary; may include foundations and set theory, graph theory, and number theory. May be repeated. Prerequisite: Mathematics 222; any additional prerequisites will be announced when scheduled.
- MATH–420 Advanced Calculus (3 credits)
- Provides more formal treatment of topics in elementary calculus, including limits, continuity, differentiability, integrability, and infinite series, with emphasis on precise definitions and proofs of theorems. Prerequisite: Mathematics 323. Fall.
- MATH–425 Complex Variables (3 credits)
- Introduction to complex numbers and the calculus of functions of a complex variable. Topics include the algebra and geometry of complex numbers, limits and derivatives of functions of a complex variable, contour integrals, Taylor and Laurent series, and residues. Prerequisite: Mathematics 323.
- MATH–431 Theory of Life Contingencies (3 credits)
- Covers the theory and application of contingency mathematics in the areas of life and health insurance, annuities and pensions, using both stochastic and deterministic approaches. Includes material covered on the Society of Actuaries Exam MLC. Prerequisite: Mathematics 330, 365. Spring 2018 (every other spring).
- MATH–445 Abstract Algebra (3 credits)
- Introduces algebraic structures and their applications. Covers set theory, number theory, modular arithmetic, groups, rings and fields. Prerequisite: Mathematics 341. Spring 2019 (every other spring).
- MATH–466 Mathematical Statistics (3 credits)
- Develops standard topics in mathematical statistics, including sampling distributions, estimation, hypothesis testing, analysis of variance, regression, and correlation. Prerequisite: Mathematics 365 with C- or better. Spring.
- MATH–490 Seminar/Workshop/Independent Study Math (0.5-3 credits)
- Seminar/workshop topics announced when scheduled. Independent study topics selected by students in consultation with the mathematics professor who supervises the work. Prerequisite: Permission of instructor.
- MATH–491 Special Topics in Advanced Math (0.5-3 credits)
- In-depth exploration of a topic not covered in other courses as preparation for graduate level mathematics. Topics vary, but may include algebraic topology, analytical number theory, coding theory, differential geometry, functional analysis, Lie theory, partial differential equations, real analysis, ring theory, and topology. May be repeated. Prerequisites: Mathematics 323; any additional prerequisites will be announced when scheduled.
- MATH–495 Senior Seminar: Mathematical Modeling (3 credits)
- Focuses on the formulation, analysis, and interpretation of mathematical models related to contemporary problems drawn from the natural sciences, social sciences, and management science. Involves team projects and a seminar format. Prerequisites: Senior standing; at least two courses chosen from Mathematics 323, 324, 365, 341, or 373; at least one computer programming class. Fall.
- MATH–499 Internship in Mathematics (1-6 credits)
- A structured assignment which allows the student to gain practical experience in a mathematics-related field relating to a career interest. The student is directed by a faculty member of the Department of Mathematics and supervised by a member of the cooperating organization. Prerequisite: Permission of Department of Mathematics.
- MATH–591 Special Topics in Math (3 credits)
- MATH–ELEC Mathematics Elective Transfer Credit (0.5-99 credits)

- STAT–266 Introductory Statistics With R (3 credits)
- This course serves as an introduction to the foundations and applications of statistics in the framework of the field of Data Science. Covering key aspects of data exploration, visualization, and traditional topics in statistical inference, this course is approached through a project-based curriculum using open data sources from various areas of application and the open-source statistical software program R.
- STAT–267 Experimental Design (3 credits)
- The thoughtful design of an experiement provides the best chance of producing meaningful, defensible evidence to answer questions of interest. This course will cover teh process of planning a well-designed experiment to collect appropriate data such that an analysis using standard statistical procedures results in valid and objective conclusions. Design methods will be applied and analyzed using a standard statistical software program, such as R.
- STAT–300 Data Analysis in the Real World (3 credits)
- This course is designed to provide student an opportunity to apply data analytics in a real world context. Students will learn to conduct a complete data analytics project, including understanding the context; collecting, organizing, visualizing, and analyzing the data; and communicating the findings to the client. Analysis will be supported using statistical software, including R. Because its overarching goals align closely with the university's GAP program, this course may be offered as a GAP course. Because the topics (and thus the necessary statistical tools) will vary with each offering, this course may be repeated.
- STAT–361 Linear Models (3 credits)
- Provides an in-depth look at linear regression models by considering both the theory of the linear model and the skills needed to conduct relevant analyses using the statistical software program R. Topics include estimation, inference, diagnostics, transformations, variable selection, ANOVA, and a brief introduction to Generalized Linear Models.
- STAT–362 Machine Learning (3 credits)
- Introduces Machine Learning and its core models and algorithms by examining techniques in both supervised and unsupervised learning. Algorithms under consideration are regression, decision trees, neural networks, support vector machines, and clustering algorithms. Concepts and algorithms will be implemented using the statistical sofware program R.
- STAT–474 Techniques for Large Data Sets (3 credits)
- This course treats methodologies and customized algorithms and tools for efficiently extracting, interpreting, and drawing inferences from very large datasets. It begins with an introduction of the Big Data problem and the limitations of applying standard statistical techniques to large datasets. It develops algorithms for Big Data analysis - including data compression, indexing, and summarization - and provides experience in using Big Data-specfic tools such as Map-Reduce and Hadoop in conjunction with the general purpose statistical software program R.
- STAT–493 Statistical Modeling (3 credits)
- Encompasses the entire cycle of a data analysis project, including problem formulation, acquisition and cleaning of data, model selection, and fitting, parameter estimation, interpretation, and reporting. Draws on multiple data analytic techniques develped across an array of statistics courses to address real-world problems. Involves team projects and a seminar format.

**Office Phone:**

812-488-1234

**Office Email:**

math@evansville.edu

**Office Location:**

Room 314, Koch Center for Engineering and Science