60 courses with the subject COMP, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
COMP 1210Introduction to Computing3
This course is for non-CS majors. The purpose is to introduce students to computer hardware and use. Topics covered include: Computer hardware, operating systems and some of the commonly used application software such as a word Processor, an Internet browser, an email manager, a presentation manager and a spreadsheet processor. Course includes hands-on work with computers. Not open to CS majors.
The purpose is to introduce students to essentials of computer hardware and software, concept of operating systems and problem modeling and solving. Topics to be offered are number representations, computer memory and data storage methods, basic digital logic, problem modeling and solving, introduction to algorithm development, basic programming skills, basics of computer operating systems, and current issues relating computing to society presented.
This course is designed to introduce programming fundamentals. Students will learn to write programs involving variable storage, formatted input/output, control structures, program repetition, logical operations, functions, file interaction, elementary data types including array and string, and aggregated data types defined by struct. Students are required to use computer labs.
This course is a computer programming for non-CS majors. Topics covered include: Introduction to computer hardware, problem solving and algorithm development, and implementation of algorithms using an object oriented programming language. Schedule will include two (2) lecture hours and one (1) lab hour.
This course is to provide students with the opportunity applying the knowledge, skills and abilities gained in classrooms and labs in Computer Science into real-world work. Students undertake learning projects in governmental, business, industry, or university settings. Formal proposals, project objectives, and learning plans must be reviewed and approved by faculty advisor. Student activities and progress are monitored, evaluated and graded by an assigned faculty.
This course is to provide students with the opportunity applying the knowledge, skills and abilities gained in classrooms and labs in Computer Science into real-world work. Students undertake learning projects in governmental, business, industry, or university settings. Formal proposals, project objectives, and learning plans must be reviewed and approved by faculty advisor. Student activities and progress are monitored, evaluated and graded by an assigned faculty.
This course is to provide students with the opportunity applying the knowledge, skills and abilities gained in classrooms and labs in Computer Science into real-world work. Students undertake learning projects in governmental, business, industry, or university settings. Formal proposals, project objectives, and learning plans must be reviewed and approved by faculty advisor. Student activities and progress are monitored, evaluated and graded by an assigned faculty.
This course is to provide students with the opportunity applying the knowledge, skills and abilities gained in classrooms and labs in Computer Science into real-world work. Students undertake learning projects in governmental, business, industry, or university settings. Formal proposals, project objectives, and learning plans must be reviewed and approved by faculty advisor. Student activities and progress are monitored, evaluated and graded by an assigned faculty.
This course presents the important topics of communications and ethics for computer professionals. Topics discussed include: Introduction and definitions, ethics for computing professionals and computer users, computer and Internet crime, privacy, freedom of expression, intellectual property, security, and the Software Engineering Code of Ethics and Professional Practice.
This course introduces fundamentals of different mathematical theories and models required for understanding Data Science related algorithms and applications. The course includes selected topics from statistics, hypothesis tests, probability distributions, Bayes’ theorem, linear algebra, matrix decomposition, graphs and trees. The course will briefly relate mathematical theories and models to Data Science applications to provide necessary foundation and preparation for higher level courses on Data Science and Machine Learning.
An opportunity for students to integrate the theory, knowledge, design and analysis ability, and programming skills gained in previous computer science work into a team-based project carried out under the supervision of a member of the Computer Science faculty. Senior project I leads to the completion of the project in COMP 4510 . Students are required to develop a written technical partial report as well as an oral status report.
COMP 4520Introduction to High Performance Computing 3
This course focuses on the fundamentals of developing, analyzing, and implementing parallel, scalable, and highly optimized algorithms for modern multi-core processors. The topics included are the study of distributed systems, resource management in shared/distributed memory systems, multiple-core computers, GPUs, computer clusters, parallel computers architectures, and synchronous/asynchronous computer networks. The problems of parallelization strategies, caching, resource allocation, synchronization, link/process failures in synchronous/asynchronous systems will also be discussed. 3
The course introduces the computing models and algorithms of distribution systems. The course also exposes students to an array of big data analysis theories, techniques and practices in different fields of study using distributed models. The topics include distributed computing models, massage-passing and shared memory systems, design and analysis of synchronous and asynchronous algorithms, fault tolerance, and data distribution, collection, processing and analysis in distributed systems. This is a project-based course that provides students with hands-on experience on distributed computing with different data types.
COMP 4770Network Programming and Information Assurance3
This course provides students fundamentals of network programming and network computing. The course reviews connection and connection-less network protocols, Winsock socket programming, network protocols, multi client-server system, peer-to-peer models, networked computer communication, coordination and information assurance through message passing and basics of cluster computing.
This course covers both the fundamentals and advanced topics in operating system (OS) security. Access control mechanisms, memory protections, and inter-process communications mechanisms will be studied. Students will learn the current state-of-the-art OS-level mechanisms and policies designed to help protect systems against sophisticated attacks.
This course is an introduction to data science and the analysis of large data sets in order to draw insights and to extract information. The course covers using Python libraries for reading large data sets including Numpy and Pandas, reading input from standardized formats, calculating statistical measures from large data sets, linear regression, logistic regression, unsupervised learning including k-means clustering, and time series analysis with relational and non-relational databases.
This course provides an introduction to machine learning with the opportunity to develop and implement data-driven solutions and predictive models for different applications. Topics broadly include: (i) supervised learning, (ii) unsupervised learning, (iii) dimensionality reduction and feature selection techniques, and (iv) best practices and model evaluation methods in machine learning. The course will also draw examples from numerous case studies and applications.
This course is an introduction to data visualization and the graphical representation of data. The growing data deluge from multiple sources require skills in representing data, in order to extract meaning and actionable intelligence from these data sets. Students learn how to communicate the relationship between data through systematic mapping between graphical representations and the underlying data values. The class teaches how representations of data can give insight and make data analysis easier.
