An introductory course in computer science for majors. Also open to nonmajors. Emphasizes computer problem-solving methods and algorithmic development. Topics include programming in Python or a comparable language, techniques of good programming style, data types, file and screen input and output, control structures, subroutines, recursion, arrays, and pointers. 4 SH. CC: Analytical Thought.

This is the second course in computer programming that builds upon functional programming developed in the prerequisite course. This class introduces object-oriented programming, stressing the importance of planning, design, and optimization of object-oriented solutions to programming problems. Topics include objects, classes, constructors, inheritance, polymorphism, and design. Prerequisite: CSCI-181 Principles of Computer Science. 4 SH.

Second course in computer programming. Stresses the interplay between algorithms, data structures and their implementations. Topics include stacks, queues, linked lists, sorting, searching, binary trees and graphs. Prerequisite: CSCI-181 Principles of Computer Science.

This course provides an introduction to the concepts in the automatic extraction of implicit, previously unknown, and potentially useful information from large data that are generated in commerce, science, and other areas. Topics include preprocessing of the data, application of the fundamental algorithms on the prepared data, and interpretation of the patterns discovered by the algorithms. The fundamental algorithms for supervised learning, including classification and numerical prediction, and unsupervised earning, which includes association rules and clustering, are introduced. Prerequisites: CSCI-181 and either MATH-180 or both MATH-108 and INFS-233. 4 SH.

Introduces the design and implementation of algorithms using an object-oriented programming language such as C++ or Java. Covers correctness and efficiency of algorithms for sorting, searching, graph problems and mathematical algorithms. Prerequisites: MATH-221 Discrete Structures and CSCI-281 Data Structures. 4 SH.

This course provides an introduction to the systematic study of algorithms and systems that improve their knowledge or performance with experience. A statistical approach that emphasizes concepts and the implementation of the methods is presented to make sense of large and complex data. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering. Prerequisites: MATH-180 Statistical Methods (or both MATH-108 Intro to Statistics and INFS-233 Data Driven Decision Making), MATH-201 Linear Algebra, and CSCI-181 Principles of Computer Science. 4 SH.

Differentiation and integration of polynomials, exponential, logarithmic and trigonometric functions, rules of differentiation, the Mean Value Theorem, L’Hôpital’s Rule, the Fundamental Theorem of Calculus, and applications. 4 SH. CC: Analytical Thought.

Techniques of integration. Also includes improper integrals, further applications of integration, and power series. Prerequisite: MATH-111 Calculus I or equivalent. 4 SH. CC: Analytical Thought.

This course provides a broad overview of introductory statistical methods and data analysis. Topics include descriptive statistics, probability, probability distributions, statistical inferences on population means and population variances, multiple comparisons, categorical data, data analysis using linear regression and multiple regression, design of experiments, and analysis of variance. 4 SH. CC: Analytical Thought.

Systems of linear equations, matrices and matrix algebra, vector spaces, linear transformations, inner product spaces, determinants, eigenvalues and eigenvectors, and selected applications. Prerequisite: MATH-111 Calculus I. 4 SH. CC: Analytical Thought.

Calculus of several variables, partial derivatives, critical points, multiple integrals, gradient, curl, divergence, Green’s, Stokes’, and Divergence Theorems. Prerequisites: MATH-112 Calculus II and MATH-201 Linear Algebra. 4 SH.

An introduction to the basic logical and set-theoretic framework of mathematics and computer science. Topics include logic, proof techniques, mathematical induction, divisibility and modular arithmetic, sets, relations, mappings, graphs, and counting principles. Prerequisite: MATH-111 Calculus I. 4 SH.

Experience in individual research and presentation of computer related topics. Prerequisites: senior standing and computer science department head’s permission. 4 SH. CC: Writing Intensive.