This course provides an introduction to fundamental operating systems concepts. Topics include the process model of computation and concurrent processes, inter-process communication and synchronization, process scheduling, deadlock, memory management, paging and segmentation, and file systems.
Business intelligence is the use of information systems to inform managerial decisions. Businesses today have access to data in unprecedented volume, but often lack the expertise to leverage data for competitive advantage. In addition, companies often miss opportunities to guide strategic decision making because they do not gather or track the correct metrics. This course provides students with the skills to gather, analyze, and transform data into meaningful information.
In a data-rich, data-driven society, it is increasingly important to be able to tell a story with data. This course introduces the student to the fundamentals of advanced data visualization techniques, using both interactive computer visualizations and publication ready charts to display data and communicate model results. Whether your interests are related to business or science, effective, accurate, and ethical communication is essential in today’s data-centric world.
The fundamentals of data structures will be studied from an object-oriented perspective. Data structures discussed will include linked lists, stacks, queues, tress, sets, maps, hash tables, heaps and graphs. Concepts such as genetic types, iterators, file compression and dynamic programming will also be addressed.
This course offers an introduction to the foundations of computing. Topics include different models of computation such as finite automata, push-down automata, Turing Machines, and regular expressions; grammars and parsing techniques; solvable and unsolvable problems; and P and NP complexity classes.
This course provides students with a hands-on experience in applying project management and systems analysis, design and implementation. Students will work with local business professionals in the design and delivery of a project.
This course is designed to teach the full-fledged software development cycle, with a team project utilizing CASE tools. Topics include testing and validation, metrics and complexity, software reliability and fault tolerance.
The objective of this course is to teach the student the basic principles involved in the design and operation of computer networks. Topics include computer network architectures and models, physical media and signaling, data link protocols, medium access control, routing and IP, transport services including TCP/UDP, network applications, local-area and wide-area networks. The course will consist of both a lecture portion and a hands-on laboratory.
Bioinformatics is the application of computer science to biology and medicine but it is also a driver of how questions are generated and answered in modern biology. The magnitude of biological data - from environmental to genomic - is growing exponentially. This course will introduce students to a varied sampling of publicly available biological data and the basic scripting skills to organize, manage, and analyze that data. They will learn about algorithm design for genome and sequence analysis, genetic variation, phylogenetics, structural, and systems biology. Students will conduct independent projects and be introduced to the highly used programming language and statistical environment R and Python.
This course introduces the student to various aspects of artificial intelligence (AI), whose goals are the creation of more useful machines by making them more "intelligent." The course focuses on the fundamentals of machine learning and uses supervised, unsupervised, and reinforcement learning algorithms for classification and prediction tasks. The student will learn to build models from data using regression, logistic regression, classification and regression trees, random forests, ensemble and boosted methods, neural network techniques, and deep learning using convolutional neural networks. These algorithms are then applied to the areas of machine vision, image feature recognition, natural language processing, and general predictive techniques used in the field of Data Science.
Special Topics in Computer Science