Department of Information Technology and Decision Sciences

This course introduces students to concepts and processes, technologies, and methodologies that are commonly used in data visualization that an organization may use to enhance its descriptive, predictive, and prescriptive methods for making fact-based decisions.

Students are introduced to prescriptive analytics through formulation and solution of mathematical models, with a particular focus on optimization models. The business use of the models, as well as their limitations, is emphasized. Topics include linear, integer, non-linear programming, network models, genetic algorithms, decision analysis, and project management models.

This course addresses advanced business analytics techniques and the application of such techniques to large data sets. Some alternative business analytics strategies are introduced. Descriptive, predictive, and prescriptive models are included. Topics covered in this course include data visualization and exploration, cluster analysis, and developing and calibrating predictive models for big data. Applications of multivariate, logistic, and probit regression to business analytics are discussed. Software packages such as SAS/JMP/SPSS may be used.

This course provides a foundational understanding of probability and statistics in a business context. Students learn to analyze data, make informed decisions, and solve business problems using statistical techniques. Topics include sampling distributions, confidence intervals, hypothesis testing, simple and multiple regressions, time series forecasting, and decision making under uncertainty. Emphasis is placed on the application of the tools to business problems.

Predictive analytics techniques for business. Applications include both shorter term forecasting for sales and operations management as well as forecasting for long term planning. Emphasis is on statistical methods to obtain and evaluate forecasts. Statistical models are implemented using standard software such as MINITAB, EXCEL, R, and/or Python.

Simulation modeling is an integral part of the analytics revolution, enabling the creation of models that can represent the variability that exists in many real business systems. This course covers the theory and application of simulation modeling, with an emphasis on how simulation provides predictive and prescriptive analytics to support business decision-making. Topics include simulation fundamentals, the project life-cycle, model development, input and output analysis, verification and validation, and the presentation of a simulation study. We utilize a major commercial simulation software package for assignments and class projects.

This course explores the intersection of intelligent systems and business analytics. Students gain insights into leveraging artificial intelligence and data analysis techniques to drive informed decision-making and solve complex business problems. Topics include descriptive analytics, predictive analytics, prescriptive analytics, AI, machine learning, and real-world case studies in various industries.

Approval for enrollment and allowable credits are determined by the department. Additional support may be provided by the Monarch Internship and Co-Op Office in the semester prior to enrollment.

Approval for enrollment and allowable credits are determined by the department. Additional support may be provided by the Monarch Internship and Co-Op Office in the semester prior to enrollment.

Advanced topics in business analytics offered periodically.

Affords students the opportunity to undertake independent study under the direction of a faculty member.

An applied study of statistical methods including analysis of variance, cross-sectional multiple regression, time series regression, panel data methods, discriminant analysis, and generalized linear models. Data analyzed using a computerized statistical package. Emphasizes development of the student's ability to use statistics for independent research.

This course introduces the fundamentals of multilevel modeling. Alternative methods of analysis are discussed and critiqued. Use of specialized multilevel modeling software is demonstrated. Topics include a detailed discussion of the issues associated with variable centering. Applications to business research investigations are emphasized.

This course covers both the theory and application of simulation modeling and analysis to business, supply chain, and logistics systems. Both discrete-event and continuous simulation modeling approaches are covered, using a major commercial simulation package. Emphasis will be on the use of simulation as a tool to support business, supply chain, and logistics decision making.

This course will explore both the conceptual and technical aspects of agent-based simulation, particularly as utilized for modeling of business systems. Students will explore the roots and literature of agent-based modeling and related fields. Students will also learn to develop agent-based simulation models using a major commercial simulation package.

The advanced study of selected topics not offered on a regular basis.

An applied study of statistical methods including analysis of variance, cross-sectional multiple regression, time series regression, panel data methods, discriminant analysis, and generalized linear models. Data analyzed using a computerized statistical package. Emphasizes development of the student's ability to use statistics for independent research.

Advanced statistical models that are commonly encountered in business research. Topics include confirmatory factor analysis as well as structural equation modeling. Emphasis is on model development as well as use of statistical software in analyzing realistic business-oriented data sets.

This course covers both the theory and application of simulation modeling and analysis to business, supply chain, and logistics systems. Both discrete-event and continuous simulation modeling approaches are covered, using a major commercial simulation package. Emphasis will be on the use of simulation as a tool to support business, supply chain, and logistics decision making.

This course will explore both the conceptual and technical aspects of agent-based simulation, particularly as utilized for modeling of business systems. Students will explore the roots and literature of agent-based modeling and related fields. Students will also learn to develop agent-based simulation models using a major commercial simulation package.

This course introduces project management principles and methodologies. Topics include project management framework, knowledge areas, and techniques. It guides students through the processes of defining, planning, executing, and controlling projects to ensure successful delivery on time, within budget, and to quality standards. The course emphasizes practical skills for managing project work and explores how Information technology tools, including artificial intelligence, can support project management tasks.

Focuses on improving business use of information technology and achieving competitive advantage. All students gain a strategic perspective on an important organizational resource--information. Prepares students for managerial positions and effective communication with executives.

This course explores the intersection of intelligent systems and business analytics. Students gain insights into leveraging artificial intelligence and data analysis techniques to drive informed decision-making and solve complex business problems. Topics include descriptive analytics, predictive analytics, prescriptive analytics, AI, machine learning, and real-world case studies in various industries.

An introduction to key concepts and techniques of cloud computing and security. Topics include: cloud computing systems, virtualization and container technologies, cloud architecture and service platform design, cloud programming models, big data analytics, cloud performance and security.

Introduction to database management systems. The topics addressed include system architecture, data models, database analysis, design and implementation, query processing, business transaction processing, and database security.

The course covers key concepts in data mining, data visualization, and decision support systems. Students will learn how to effectively harness data-driven technologies to optimize business processes and enhance organizational performance.

The course introduces students to distributed computing systems, cloud computing, data storage solutions, and network architectures that underpin Big Data processing. Students will explore the technological infrastructure and computational techniques required to handle enormous volumes of data efficiently.

Information and Communication Technologies (ICT) is a critical enabler of the digital enterprise. This class introduces cutting-edge ICT, including enterprise systems, IoT, CPS as the foundation for digitalizing enterprises for the seamless integration of enterprises and supply chain. Topics includes intra- and inter-organizational integration, supply chain collaboration and integration, and digitalization technologies.

This course provides knowledge of project management including tools and techniques to manage scope, time, cost, quality, risk, team, communications, security and procurement. Special issues in the context of information- and technology-based projects are emphasized.

Approval for enrollment and allowable credits are determined by the department in the semester prior to enrollment. Available for pass/fail grading only.

Approval for enrollment and allowable credits are determined by the department in the semester prior to enrollment. Available for pass/fail grading only.

Introduction to enterprise architectures for business organizations as well as related information architectures. Examines traditional techniques as well as emerging techniques including industrial information integration engineering.

Overview of computing aspects of medical informatics. Computational methods in scientific computing of medical informatics are covered. The basic thrust is to demonstrate the usefulness and power of computational methods in solving real-life problems in perspectives of medical informatics.

3 credits.

Affords students the opportunity to undertake independent study under the direction of a faculty member.

3 credits.

1-6 credits.

3 credits.

This course introduces the foundations and process of academic research, providing a comprehensive overview of research methodologies in Information Technology. It examines empirical, behavioral, conceptual, and interdisciplinary approaches. Key topics include developing research questions, designing appropriate methodologies, conducting empirical investigations, and critically evaluating scholarly work.

This doctoral seminar introduces students to research in the Information Systems (IS) discipline, emphasizing the societal impact of IS and artificial intelligence (AI). Students critically examine key streams of empirical IS research and explore emerging topics and challenges related to technology and society. The course also develops students’ skills in analyzing and writing empirical research papers.

This seminar provides an overview of major research areas in Information Technology. It introduces students to key themes, theoretical foundations, and emerging trends in IT research. Topics include information security, artificial intelligence, system development, ethics, and other contemporary issues shaping the IT discipline. The course is designed to help doctoral students develop a broad understanding of the IT research landscape and identify potential areas for future investigation.

This seminar provides a foundation in the theories, methods, and research practices of business intelligence with an emphasis on empirical analysis. The course integrates econometric fundamentals, the use of analytical software for data analysis, and engagement with contemporary scholarly work in business intelligence. Students will develop the methodological and conceptual skills necessary to design and carry out independent, original research in business intelligence and related fields.

The course examines the latest advances in knowledge management (KM) including identifying, capturing, sharing and evaluating an enterprise's knowledge assets. The course reviews and discusses existing technologies in KM and new emerging KM technologies and practices.

This course examines how supply chain management and information technology integrate to support global supply chain design and management. Topics include the theories and practices of how information technology enables effective material flow, e-commerce and retailing, logistics and maritime activities, supply chain network planning and management, supply chain disruption management, procurement, production, and inventory management. A wide variety of research methodologies are explored as well as coverage of advanced technologies such as AI and machine learning, IoT, blockchain, and big data.

3 credits.

Ph.D. level research and writing of dissertation.

This course is a pass/fail course for master's students in their final semester. It may be taken to fulfill the registration requirement necessary for graduation. All master's students are required to be registered for at least one graduate credit hour in the semester of their graduation.