Information Systems Management (ISM)

Prerequisite: 24 credits. Provides a broad overview of e-business strategies, products and technologies. Discusses transformation of marketing, production and other business functions; and related legal, political, ethical and cultural issues. (Formerly 6100:201)

Prerequisites: Admission to the Applied Artificial Intelligence and Information Systems major and proficiency in using a PC and some basic knowledge of using Excel. Pre/Corequisite: ACCT 250. This is an introductory course to Applied Artificial Intelligence and its applications to business, including the foundational concepts of Information Systems such as data models, information security, systems integration and architecture. It is designed to serve two purposes. First, it teaches students to be literate on artificial intelligence, information systems, and tools and techniques to become managerial end users of AI and IS techniques. Second, it introduces the fundamental concepts and techniques and teaches students to become decision makers in the management of artificial intelligence integration and applications.

Prerequisites: Completion of 48 credit hours and [ACCT 250 or admission to the Computer Science major]. Provides a technical and organizational foundation for understanding the use and importance of information systems and information technology in today's business environment. (Formerly 6500:310)

Prerequisites: ACCT 250 and 48 completed hours. Analysis and automation of business operations and processes. Development of applications based on a simulated enterprise-wide database. (Formerly 6500:315)

Prerequisites: [ACCT 250 and 48 completed hours] or [admission to the College of Engineering and Polymer Science with 48 credit hours completed]. An introduction to database design and management, including data modeling, relational theory, Structured Query Language, and database applications, development, using database management systems. (Formerly 6500:324)

Prerequisites: ISM 315. An introduction to the techniques of business modeling, systems design, and implementation, including the application of software engineering tools in support of modeling and code generation. (Formerly 6500:325)

Prerequisites: Admission to the Applied Artificial Intelligence and Information Systems major and ISM 250. While enterprise databases store and supply data for operations, AI-based analytical applications require greater volume, variety, and velocity of data provision. This course prepares students to understand and work with a variety of data models and storage approaches; prepare, extract, transform, and combine data in data pipelines; and consider how to ensure quality, scale, and manage data using principles of data governance.

Prerequisites: Admission to the Applied Artificial Intelligence and Information Systems major, ISM 250, and MGMT 305. Artificial Intelligence (AI) is a study on how to create programs (agents or robots) that intake information and take actions without programming. AI enables businesses to be competitive today and will become a necessity for them to survive in the future. This course introduces the fundamental concepts and problem-solving techniques of AI in business contexts and studies how AI algorithms and models may be used to transform business processes. It covers data-light version of problem-solving approaches such as search, optimization, and constraint satisfaction and emphasizes big data version of automated decision-making, inference-making, and knowledge acquisition, representation, and integration via propositional logic, Bayesian networks, neural networks, fuzzy logic, and evidential theory situated in challenges posed to organizations. This course uses Python programming for exercises. 

Prerequisites: Junior or greater standing and ISM 310. Principles of the design and management of data networks for business communications. (Formerly 6500:420)

Prerequisites: Admission to the Applied Artificial Intelligence and Information Systems major, ISM 326, and ISM 360. This course studies a branch of machine learning of business knowledge via neural network models and its applications to business analytics. It covers multilayer perceptrons for prediction and classification, convolutional neural networks for computer vision, recurrent neural networks for language processing, deep belief networks, and attention and transformer architectures for large language models situated in an organizational environment. This course uses Python programming for projects. 

Prerequisites: ISM 324 and MGMT 305. Examines managerial and technical aspects of business decision-making based on the use of data warehouses, on-line analytical processing (OLAP) and data mining. (Formerly 6500:425)

Prerequisites: Junior or greater standing and ACCT 250. Students will gain an understanding of issues and skills related to web application design and development. (Formerly 6500:426)

Prerequisite: ISM 315. The course provides an understanding of issues and underlying application integration. Topics include coverage of middleware technologies, B2B standards and XML. (Formerly 6500:427)

Prerequisites: ISM 324 and ISM 325. Pre/Corequisite: ISM 427. Implementing business objects and use cases in projects. Object persistence, object collaboration, and controller and UI designs are discussed. (Formerly 6500:428)

Prerequisite: Permission of department chair or designated faculty member. On the job experience with public or private sector organizations. (Formerly 6500:488)