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This course will examine the structure of a computer game -- engines, assets, libraries, and other software resources. In addition, the tools necessary to build these assets and assemble them into a playable entity will be explored. The lectures and text examine game development, design, testing, and distribution at a higher, abstract level, while the project component provides practical experience. The project requires students to work with an existing code base, as most entry level positions will require, so that adequate preparation is provided for an industrial position. 

This course will consider the analysis of algorithms that arise in a number of areas of computer science. Examples include searching and sorting algorithms, algorithms in scientific computing, and algorithms from discrete mathematics. The analyses will primarily focus on time complexity analysis, i.e., the determination of the number of fundamental operations required to apply the algorithm to a problem of a given size. In some cases, the theoretical analyses will be supported experimental investigations.

This course is designed to introduce the student to some of the key concepts in computing science and simultaneously provide introductory hands-on experience using a modern programming language.

This is a second year course, required for all Computing Science majors. It is the sequel to CSCI2327 and focuses on assembly language programming for the intel architecture.

The ubiquitous usage of computing and communication technology is resulting in an unprecedented generation of data. The increased storage and computational capacity makes it possible to sift through this mountain of data and identify knowledge nuggets that can be used for improvements in productivity and customer service. This course introduces five important aspects of data mining: clustering, classification, association, predictions, and sequential mining. We will study the theoretical foundations of these techniques, and apply them in practical situations. Relevant introduction to database management and data preparation will be also provided. There is a considerable emphasis on the applications through assignements and projects. The datasets that will be used can be categorized as: private retail dataset, government published demographical statistical datasets, and web datasets.

Selected Topics in Numerical Analysis [CSCI 2309] 3 credit hours Prerequisite: MATH 2308 [CSCI 2308] or permission of instructor. Select topics for MATH 2308 [CSCI 2308] may be further explored. Other topics may include linear least squares, eigenvalues and eigenvectors and optimization.