All Results
Computer Information ScienceCredits
An introduction to all important aspects of software engineering. The emphasis is on principles of software engineering including project planning, requirements gathering, size and cost estimation, analysis, design, coding, testing, implementation, and maintenance. Group project work.
This course is designed to give students the skills required to design and develop video games. The primary focus of the course is on mobile game development, game design principles and user-centered design methodologies. A play-centric approach to game design and development will be studied, discussed and applied in the production of a game demo.
Special topics not covered in other courses. May be repeated for credit on each new topic.
Research methodology in general and in computer science. Data and research sources. Analysis of existing research. Preliminary planning and proposals. Conceptualization, design, and interpretation of research. Good reporting. Same as CS 600. Pre-req: An elementary statistics course.
Special topics in computer science research not covered in other courses. May be repeated for credit on each new topic.
Students attend seminar presentations and present a research topic at one of the seminars. Same as CS 602. Pre-req: consent
This course is a continuation of Artificial Intelligence (IT 530). Emphasis is placed on advanced topics and the major areas of current research within the field. Theoretical and practical issues involved with developing large-scale systems are covered. Same as CS 630. Pre-req: IT 530
- Prerequisites:
- CIS 518
- Programs:
The design of large-scale, knowledge¿based data mining. Emphasis on concepts and application of machine learning using big data. Examination of knowledge representation techniques and problem¿solving methods used to design knowledge¿based systems. Pre-req: instructor permission required
- Prerequisites:
- CIS 518
- Programs:
This course combines theory with hands-on projects in modern computer vision techniques. It covers both foundational and advanced topics, including deep learning, image processing, feature detection and matching, object detection, segmentation, and recognition. The focus is on the practical application of Convolutional Neural Networks and Generative Adversarial Networks in computer vision, while also exploring image generators and addressing the ethical and legal challenges related to synthetic images.
- Prerequisites:
- CIS 631
- Programs:
This course explores both the theoretical foundations and practical applications of Natural Language Processing (NLP). Key topics include text processing, language models, sequence-to-sequence models, sentiment analysis, named entity recognition, and machine translation. The course also covers advanced techniques for building and fine-tuning large language models, such as recurrent neural networks, transformers, reinforcement learning, and retrieval-augmented generation. Through hands-on projects and case studies, students will apply their knowledge to build, optimize, and deploy NLP applications, while assessing their ethical implications.
- Prerequisites:
- CIS 631
- Programs:
This course prepares students to tackle the ethical, legal, and technical challenges of AI technologies, focusing on issues such as bias, privacy, accuracy, security, and misinformation. Students will explore methods to identify and mitigate bias in AI models, alongside techniques for ensuring data privacy. The course also covers the application of interpretable machine learning and explainable AI techniques and provides a critical examination of data governance frameworks and regulatory guidelines for responsible AI deployment and audits.
- Prerequisites:
- CIS 518
- Programs:
In-depth study of advanced topics such as object-oriented databases, intelligent database systems, parallel databases, database mining and warehousing, distributed database design and query processing, multi-database integration and interoperability, and multilevel secure systems.
- Programs:
In this course, students will design and implement distributed big data architecture. The architecture consists integration of homogenous and heterogeneous databases and other structured and unstructured data sources. Students will apply concepts of distributed recovery and optimization, and other related topics.
- Prerequisites:
- CIS 540
- Programs:
Content covered will include the following: scientific process; sampling bias; hypothesis tests; confidence intervals; risk analysis vs assessment; statistical analysis concepts. Issues with qualitative and quantitative risk analysis methodologies. Exposure to and practice with multiple risk analysis methodologies, including at least one that is considered a standard.
This course examines the organizational leadership structure and competencies of healthcare and/or IT organizations, the governance planning process, financial management, ethical and legal decision-making, privacy, and data-based best practices that balance organizational and regulatory requirements with feasible cost-effective solutions.
- Programs:
Content covered will include the following: analyze audience; define report outline and objectives for target audience (IT, executives, audit & compliance); ethos/pathos/logos concepts; white papers. Data misrepresentations, intentional or unintentional; appropriate use of data visualization tools and dashboards; representing needle in haystack data (low volume, high risk).
- Programs:
Risk management strategies. Human factors, resistance to change. Design, development and evaluation of security controls; catalog of security controls; performance metrics. Management oversight; cost-benefit analysis, business impact analysis; policies, processes, standards. Technical, administrative, physical controls.
This course will focus on research, design, and analysis of computer networks and data communications systems. The course will also entail detailed examination of modern communication standards, protocol systems and their implementation. Additional topics may include transmission technology, packet switching, routing, flow control, and protocols. Same as CS 662. Pre-req: IT 562 or 564
- Programs:
Problems on an individual basis. Pre-req: consent
Advanced software design, analysis, and development techniques under realistic time and budget constraints. Hands-on project management techniques. Emphasis of concepts through immersion in a team project of significant size. Same as CS 680. Pre-req: IT 580
- Programs:
Statistical package programs used in data collection, transformation, organization, summarization, interpretation and reporting, statistical description and hypothesis testing with statistical inference. Interpreting outputs, Chi-square, correlation, regression, analysis of variance, nonparametrics, and other designs. Accessing and using large files (U.S.Census data, National Health Survey, etc.). Same as CS 690. Pre-req: a statistics course
- Prerequisites:
- CIS 518
- Programs:
A course designed to upgrade the qualifications of persons on-the-job. Pre-req: consent
Student will integrate their health-related background with the practical application of scientific and professional knowledge, behavior, and skills. Students will employ health advocacy strategies, principles of quality improvement, healthcare policy knowledge, and cost-effectiveness as part of an inter-professional team to analyze data and develop a strategy to impact practice improvements in order to increase the quality and efficiency of healthcare delivery, improve satisfaction, or manage health-related costs.
- Programs:
Preparation of a master's degree alternate plan paper under the direction of the student's graduate advisor. Pre-req: consent
Provides students with opportunity to utilize their training in a real-world business environment working under the guidance and direction of a faculty member. (A maximum of 4 credits apply toward a degree in this department.) Pre: consent Fall, Spring, Summer