Master of Science (M.S.) Major in Data Analytics and Information Systems (Thesis Option)

ISAN 5199B. Thesis.

This course provides ongoing enrollment for graduate students completing a thesis as part of the master’s degree program. Students conduct approved independent research under faculty supervision, complete data collection and analysis, and prepare the written thesis document. Enrollment in this course reflects progress toward completion of the thesis. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded based on a credit (CR), progress (PR), or no credit (F) and may be repeated as necessary.

ISAN 5240. Executive Insights through Advanced Analytics.

This course equips executives with the essential tools and concepts in business analytics to make data-driven decisions. Focused on practical applications, it covers descriptive, predictive, and prescriptive analytics, and application to managerial cost and revenue management. Through hands-on learning, case studies, and real-world examples, participants will gain the skills to interpret data, apply analytics models, and integrate AI into strategic decision-making.

ISAN 5299B. Thesis.

This course provides ongoing enrollment for graduate students completing a thesis as part of the master’s degree program. Students conduct approved independent research under faculty supervision, complete data collection and analysis, and prepare the written thesis document. Enrollment in this course reflects progress toward completion of the thesis. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded based on a credit (CR), progress (PR), or no credit (F) and may be repeated as necessary.

ISAN 5318. Artificial Intelligence in Digital Economy.

This course provides an understanding of the issues in managing organizations' artificial intelligence (AI) and information assets. The course examines users' issues and challenges within the Information Technology management arena as part of a firm's business and AI strategy. The course provides frameworks and management principles that current or aspiring managers can employ with the challenges of implementing rapidly advancing AI technology. Through real-world case studies, students are empowered to effectively leverage AI to drive innovation, enhance decision-making, and automate business operations. Prerequisite: B A 5351 with a grade of "C" or better.

ISAN 5355. Database Management Systems.

This course explores the concepts, principles, issues, and techniques for managing data resources using database management systems. Topics include techniques for analysis, design, and development of database systems, creating and using logical data models, database query languages, and procedures for evaluating management software. Students will develop a management information system.

ISAN 5357. Computing for Data Analytics.

This course introduces programming for data analytics using the Python language. Students develop programs to manage data structures, perform data manipulation and cleaning tasks, and implement foundational software development techniques. The course examines methods for transforming raw datasets into structured information suitable for analysis, visualization, and reporting. Topics include variables, control structures, functions, file processing, and the use of programming libraries relevant to data analytics. Through hands-on exercises, students apply programming concepts to data-oriented and business-related problems.

ISAN 5358. Agile Project Management For Business Professionals.

This course provides an in-depth study of the project management body of knowledge as applied to Information Technology, emphasizing Agile methodologies and the processes of managing scope, costs, schedules, quality, and risks. Topics Include program management, system planning and design methodologies, material & capacity requirements, human, cultural, & international issues, and their impact on the organization.

ISAN 5360. E-Commerce: Strategies, Technologies, and Applications.

This course is designed to familiarize students with current and emerging e-commerce technologies. Topics include Internet technology for business advantage, reinventing the future of business through e-commerce, business opportunities in e-commerce, and social, political, global, and ethical issues associated with ecommerce.

ISAN 5364. Data Warehousing.

This course examines current and emerging data warehousing technologies and their use in organizational information systems. Students explore the data warehouse development life cycle and compare transactional systems with informational architectures. Topics include dimensional modeling, schema design, data integration, data quality, query performance, and cloud-based storage platforms. Through hands-on activities, students design and implement data warehouse structures, execute analytical queries, and create data visualizations for organizational and business contexts. The course also examines the role of data warehouses in reporting, business intelligence, and decision-support processes. Prerequisite: ISAN 5355 with a grade of "C" or better.

ISAN 5365. Developing Generative AI Solutions for Business and Innovation.

This course equips students with the skills and knowledge to develop advanced generative AI applications. Key topics include deploying large language models on cloud-based platforms, exploring natural language processing (NLP) techniques, and mastering prompt engineering to generate both text and code. Through hands-on projects, students integrate application programming interfaces (APIs) and implement solutions such as Retrieval Augmented Generation (RAG) to create scalable AI systems that address real-world challenges. Prerequisite: ISAN 5357 and ANLY 5336 both with grades of "C" or better.

ISAN 5367. Machine Learning.

This course examines machine learning methods used for analysis of large and complex datasets. Students apply statistical and computational techniques including regression, classification, clustering, neural networks, and ensemble methods. Topics include text processing, recommendation systems, model evaluation, feature selection, and distributed computing frameworks for large-scale data analysis. Students also develop machine learning pipelines for data preparation, model implementation, validation, and performance assessment within organizational and analytical contexts. The course includes supervised and unsupervised learning approaches used in contemporary data analytics applications. Prerequisite: ISAN 5357 and ANLY 5336 both with grades of "C" or better.

ISAN 5368. Information Security.

This course examines the analysis, design, implementation, and management of information security systems within organizational communication networks. Topics include risk management frameworks, cryptographic principles, physical and hardware security, and legal, ethical, and professional considerations affecting information security practice. Students analyze security architectures and governance approaches used to protect information assets and manage security risks in organizational environments.

ISAN 5369. Independent Study in Information Systems.

This course provides an opportunity for faculty-supervised independent study in a selected area of information systems. Students pursue in-depth research or applied project work focused on specialized topics of interest and develop information systems approaches for organizational or technical contexts. Emphasis is placed on independent inquiry, methodological rigor, and critical evaluation of results. The course may be completed individually or in small teams and may be repeated with departmental approval when the topic or information systems focus differs. Prerequisite: Instructor approval.

ISAN 5370. Enterprise Resource Planning and Business Intelligence.

This course uses information technology integrations in enterprises for operational control and business intelligence is examined via Enterprise Resource Planning (ERP) applications in customer relationships management, accounting, finance, purchasing, production control, sales, marketing, and human resource management. Emphasizes managerial issues surrounding the need, selection, and implementation of ERP systems.

ISAN 5371. Accounting Information Systems and Controls.

This course examines accounting information systems and controls and their role in the current technology-intensive business environment. Emphasis is placed on contemporary technology and applications, information technology and business information systems assessments, design of internal controls to satisfy regulation and policy requirements, control concepts, theories, and processes, information systems auditing, systems development life cycle, and information structure, data transfer, and transaction cycles. Prerequisite: ACC 3313 or ACC 5361 either with a grade of "C" or better.

ISAN 5378. Information Security Policies and Compliance.

This course focuses on the technology and managerial issues related to information policies, regulations, and compliance that assure confidentiality, integrity, and availability of data and computer systems. Topics include information security policy, regulations, laws, standards, framework, compliance, and governance. Prerequisite: ISAN 5368 with a grade of "C" or better.

ISAN 5390A. Introduction to Design Thinking.

This course introduces design thinking as a human-centered approach to problem solving that emphasizes creativity, empathy, and iteration. Students explore key design thinking tools and methods used to address complex challenges in business and organizational contexts. Through hands-on exercises and case studies, students learn to identify user needs, generate ideas, prototype solutions, and test improvements while developing collaborative, interdisciplinary, and iterative approaches to innovation in products, services, and organizations.

ISAN 5390B. Business Data Visualization for Decision Making.

This course examines methods and technologies used to communicate data through visual formats in organizational contexts. Students analyze chart selection, dashboard design, visual encoding, and data presentation techniques used in business and analytical environments. Topics include design principles, preattentive attributes, color theory, visual exploration, and interactive dashboard development using contemporary software tools. Students also evaluate visualization effectiveness, data interpretation issues, and communication practices associated with quantitative information and decision-support processes.

ISAN 5395. Internship in Information Systems.

This course provides supervised experiential learning through an approved internship in information systems. Students apply information systems concepts, tools, and techniques in professional settings while examining relationships between academic study and workplace practices. Emphasis is placed on integration of professional experience with information systems methods, documentation, communication, and reflective analysis of workplace activities. The internship is completed with an external organization under faculty supervision according to departmental internship requirements and evaluation procedures. Prerequisite: Instructor approval.

ISAN 5399A. Thesis.

This course represents a graduate student’s initial enrollment in the master’s thesis. Students begin formal thesis work under the supervision of a faculty thesis committee by identifying a research topic, reviewing relevant scholarly literature, and developing an approved research proposal. The course establishes the foundation for subsequent thesis research and writing in Information Systems. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded on a credit (CR), progress (PR), or no credit (F) basis.

ISAN 5399B. Thesis.

This course represents a student’s continued enrollment in the master’s thesis. Students remain enrolled while conducting approved independent research under the supervision of a faculty thesis committee. Activities include evaluating research quality, completing analysis, preparing the written thesis document, and revising work based on committee feedback. Enrollment in this course reflects progress toward completion of the thesis. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded on a credit (CR), progress (PR), or no credit (F) basis and may be repeated as necessary.

ISAN 5599B. Thesis.

This course represents a student’s continued enrollment in the master’s thesis. Students remain enrolled while conducting approved independent research under the supervision of a faculty thesis committee. Activities include evaluating research quality, completing analysis, preparing the written thesis document, and revising work based on committee feedback. Enrollment in this course reflects progress toward completion of the thesis. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded on a credit (CR), progress (PR), or no credit (F) basis and may be repeated as necessary.

ISAN 5999B. Thesis.

This course represents a student’s continued enrollment in the master’s thesis. Students remain enrolled while conducting approved independent research under the supervision of a faculty thesis committee. Activities include evaluating research quality, completing analysis, preparing the written thesis document, and revising work based on committee feedback. Enrollment in this course reflects progress toward completion of the thesis. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded on a credit (CR), progress (PR), or no credit (F) basis and may be repeated as necessary.

ANLY 5199B. Thesis.

This course represents a graduate student’s initial enrollment in a master’s thesis sequence. Students begin formal thesis work under the supervision of a faculty thesis committee by identifying a research topic, reviewing relevant scholarly literature, and developing an approved research proposal. The course establishes the foundation for subsequent thesis research and writing in the data analytics field. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded on a credit (CR), progress (PR), or no credit (F) basis.

ANLY 5299B. Thesis.

This course represents a graduate student’s initial enrollment in the master’s thesis. Students begin formal thesis work under the supervision of a faculty thesis committee by identifying a research topic, reviewing relevant scholarly literature, and developing an approved research proposal. The course establishes the foundation for subsequent thesis research and writing in the data analytics field. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded on a credit (CR), progress (PR), or no credit (F) basis.

ANLY 5330. Statistical Computing.

This course explores the intersection of programming and computational techniques essential for rigorous statistical analysis. Students master data manipulation, complex data structures, and algorithmic development alongside the mathematical foundations of matrix operations and numerical linear algebra. The course examines Monte Carlo simulations and numerical optimization as foundational methods for statistical modeling. Students develop an understanding of how computational procedures and numerical methods support advanced analytics and machine learning applications.

ANLY 5332. Optimization for Business Analytics.

This course introduces optimization theory and methods for modeling, analyzing, and solving complex business decision-making problems. Emphasis is placed on formulating real-world managerial problems as mathematical optimization models and applying appropriate solution techniques. Topics include linear programming, network optimization, integer and mixed-integer programming, nonlinear optimization, and selected advanced topics such as multi-objective, stochastic, and robust optimization.

ANLY 5334. Statistical Methods for Business.

This course provides a comprehensive quantitative foundation for business analytics and data-driven decision-making. Students explore essential topics such as inferential statistics, regression analysis, and various statistical modeling techniques used to solve complex business problems across functional areas. Significant emphasis is placed on understanding core statistical concepts, applying appropriate methods, and interpreting results within real-world business contexts. The curriculum focuses on analytical reasoning and evidence-based evaluation rather than prescriptive managerial conclusions, ensuring learners can critically assess data to support organizational objectives.

ANLY 5335. Forecasting and Simulation.

This course covers forecasting and simulation methods designed to analyze uncertainty and support organizational planning and decision-making. Students explore time series forecasting, causal forecasting, and both discrete-event and continuous-event simulation. Significant emphasis is placed on understanding model assumptions, selecting appropriate techniques, and interpreting results within diverse business contexts. The curriculum focuses on rigorous analytical modeling and evaluation rather than prescriptive managerial outcomes. By mastering these quantitative methods, students develop the skills necessary to navigate complex predictive scenarios.

ANLY 5336. Analytics.

This course covers analytics as the essential process of transforming raw data into actionable information to support strategic decision-making. Students explore foundational analytics concepts, data visualization, various applications, and the inherent challenges associated with modern business data. Participants develop practical skills in using analytical software, performing rigorous data analysis, and communicating results effectively. Emphasis is placed on analytical reasoning, the interpretation of complex data, and the clear presentation of insights within business contexts, ensuring students can drive organizational value through evidence.

ANLY 5337. Supply Chain Analytics.

This course examines the application of data analytics tools and quantitative methods to analyze supply chain performance at strategic, tactical, and operational levels. Topics include performance measurement, demand planning, inventory management, logistics optimization, and supply chain risk analysis from an analytics perspective. Students use statistical analysis, optimization, and simulation techniques to analyze data and support decision-making across integrated supply chain processes. Prerequisite: ANLY 5334 with a "C" or better. Corequisite: ANLY 5335 with a grades of a "C" or better.

ANLY 5338. Operations Management.

This course introduces the concepts and strategies used to design, manage, and continuously improve the processes that create and deliver goods and services. The course examines operational and tactical challenges organizations face and explores both qualitative and quantitative approaches to addressing them. Students analyze how process decisions influence organizational performance while considering emerging technologies, digital transformation, and data-enabled operational practices across diverse organizational settings.

ANLY 5339. Analytics Applications in Supply Chain Management.

This course examines the application of descriptive, predictive, and prescriptive analytics within various supply chain management contexts. Students analyze complex case studies and diverse datasets to evaluate planning, coordination, and operational challenges across global supply chain processes. Significant emphasis is placed on applying analytical techniques, artificial intelligence methods, and advanced software tools to model systems, interpret results, and assess alternative approaches. The curriculum focuses on rigorous analytical reasoning and evidence-based evaluation rather than prescriptive managerial decisions. Prerequisite: ANLY 5337 with a grade of a "C" or better.

ANLY 5342. Probability and Statistical Models.

This course covers probability theory and statistical modeling techniques essential for advanced data analysis. Students explore probability distributions, general and generalized linear models, mixture and hierarchical models, and various related extensions. Significant emphasis is placed on rigorous model formulation, interpretation, selection, and validation. The curriculum focuses on understanding the underlying assumptions and inherent limitations of statistical models while applying appropriate methods to analyze complex datasets. By mastering these concepts, students develop the analytical skills necessary to extract meaningful insights from sophisticated data structures.

ANLY 5343. Data Mining.

This course examines data mining concepts and techniques used to analyze large, complex datasets. Students explore key topics including classification, clustering, association analysis, and text mining. Significant emphasis is placed on understanding algorithmic foundations, model selection, and performance assessment. Students apply these data mining methods to analyze real-world datasets and interpret results within applied analytics contexts. Throughout the curriculum, students pay close attention to methodological assumptions and limitations, ensuring a robust and critical approach to extracting meaningful patterns from massive amounts of data. Prerequisite: ANLY 5336 with a grade of "C" or better.

ANLY 5369. Independent Study in Analytics.

This course provides an opportunity for faculty-supervised independent study in a selected area of analytics or quantitative methods. Students pursue in-depth research or applied project work focused on a specialized topic of interest, using appropriate analytical tools and techniques. Emphasis is placed on independent inquiry, methodological rigor, and critical evaluation of results. The course may be completed individually or in small teams and may be repeated with departmental approval when the topic or analytical focus differs. Prerequisite: Instructor approval.

ANLY 5395. Internship in Analytics.

This course provides supervised experiential learning through an approved internship in analytics or quantitative methods. Students apply analytical concepts, tools, and techniques in a professional setting while reflecting on the relationship between academic training and workplace practice. Emphasis is placed on integrating professional experience with analytical reasoning, documentation, and communication of work performed. The internship is completed with an external organization under faculty supervision. Prerequisite: Instructor approval.

ANLY 5399A. Thesis.

This course represents a graduate student’s initial enrollment in the master’s thesis. Students begin formal thesis work under the supervision of a faculty thesis committee by identifying a research topic, reviewing relevant scholarly literature, and developing an approved research proposal. The course establishes the foundation for subsequent thesis research and writing in the data analytics field. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded on a credit (CR), progress (PR), or no credit (F) basis.

ANLY 5399B. Thesis.

This course represents a graduate student’s initial enrollment in the master’s thesis. Students begin formal thesis work under the supervision of a faculty thesis committee by identifying a research topic, reviewing relevant scholarly literature, and developing an approved research proposal. The course establishes the foundation for subsequent thesis research and writing in the data analytics field. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded on a credit (CR), progress (PR), or no credit (F) basis.

ANLY 5599B. Thesis.

This course represents a graduate student’s initial enrollment in the master’s thesis. Students begin formal thesis work under the supervision of a faculty thesis committee by identifying a research topic, reviewing relevant scholarly literature, and developing an approved research proposal. The course establishes the foundation for subsequent thesis research and writing in Data Analytics field. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded on a credit (CR), progress (PR), or no credit (F) basis.

ANLY 5999B. Thesis.

This course represents a graduate student’s initial enrollment in the master’s thesis. Students begin formal thesis work under the supervision of a faculty thesis committee by identifying a research topic, reviewing relevant scholarly literature, and developing an approved research proposal. The course establishes the foundation for subsequent thesis research and writing in Data Analytics field. No thesis credit is awarded until the thesis is completed, approved, and submitted for binding. The course is graded on a credit (CR), progress (PR), or no credit (F) basis.