Repeatable.

Grading Basis: Satisfactory/Unsatisfactory

Repeatable. Max Credits: 9.

Join your classmates in an international travel study course to understand the business operations of another culture. Repeatable.

Grading Basis: Letter Grade

Repeatable. Max Credits: 3.

Restriction: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School.

Examines transformative technologies impacting globalization, such as Artificial Intelligence and Blockchain, how they are driving instant access to information, boosting transaction speed, and broadening the scope and reach of business across borders. Cross-listed with INTB 6600.

Grading Basis: Letter Grade

Restriction: Restricted to graduate majors and NDGR majors with a sub-plan of NBA or NBD within the Business School.

Provides a conceptual overview of statistical thinking and its applications to business problems. Topics include descriptive statistics, data exploration, probability, inferential methods, regression analysis, classification, regression with high dimensional data, etc. Students gain hands-on experience with data analytic problems via projects using real business settings and data. Note: Quantitative background in areas such as algebra, calculus or professional experience working with analytics, is recommended for success in this course.

Grading Basis: Letter Grade

Restricted to graduate majors and NDGR majors with a sub-plan of NBC within the Business School, and Data Science and Computer Science graduate majors.

Explores computing, programming, and artificial intelligence for business analytics. Students gain hands-on experience with Python, databases, and generative AI tools. Topics include the business analytics process: data acquisition, cleaning, organization, and visualization, along with predictive modeling techniques such as regression and classification. The course emphasizes applying AI and large language models to automate tasks, generate insights, and support data-driven business decision-making.

Grading Basis: Letter Grade

Restricted to graduate majors and NDGR majors with a sub-plan of NBC within the Business School, and Data Science and Computer Science graduate majors.

Time series analysis is critical to industries such as finance, marketing, retail, and accounting. This course introduces time-series models with emphasis on their practical applications in business. The goal is to show how dynamic financial and economic data can be modeled and analyzed using proper statistical techniques. The topics include methods for trend and seasonal analysis and adjustment, modeling and forecasting with autoregressive moving average (ARMA) processes, and model identification and diagnostics for time series. Other subjects include volatility and state space models. This course provides hands-on experience by pairing lectures on methodology with lab sessions using R to perform real-world data analyses. If you do not meet the prerequisites you may contact the instructor for permission to register. Note: Can only receive credit for either BANA 6630/DSCI 6230.

Grading Basis: Letter Grade

Prereqs: BANA 6610. Restrictions: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School.

Typically Offered: Spring.

Introduces a quantitative approach to business decision making under conditions of risk and uncertainty. Emphasis will include introductions to decision analysis theory, risk analysis, utility theory, multi-criteria decision making, Bayesian decision analysis and hierarchical structured models. Psychological issues and qualitative approaches in the decision-making process will be discussed. Student computer-assisted projects are included. Emphasizes how modern decision analysis integrates artificial intelligence and machine learning techniques for optimizing decisions under uncertainty. Students learn to represent decision problems using influence diagrams and to apply reinforcement learning methods for sequential decision-making, adaptive policy optimization, and exploration–exploitation tradeoffs in dynamic environments. Examples and labs illustrate how AI-powered decision systems can learn from data to improve decision quality, robustness, and utility under risk and ambiguity. Recommended prior coursework: BANA 6610 and BANA 6620.

Grading Basis: Letter Grade

Restricted to graduate majors and NDGR majors with a sub-plan of NBC within the Business School, and Data Science and Computer Science graduate majors.

Introduces the topic of Project Management (PM) in a business environment. Emphases will include the knowledge, skills, tools, and techniques as presented in the Project Management Body of Knowledge (PMBOK), a variety of managerial aspects commonly encountered in PM, and current extensions of PM. Projects in diverse contexts are examined. Cross-listed with URPL 6249.

Grading Basis: Letter Grade

Restricted to graduate majors and NDGR majors with a sub-plan of NBC within the Business School, and Data Science and Computer Science graduate majors.

Focuses on predictive analytics using AI and machine learning techniques for real-world business applications. Students learn methods such as logistic regression, decision trees, clustering, and deep learning, while integrating generative AI and large language models for automation and insight generation. Hands-on projects use business datasets to apply predictive and AI-driven solutions effectively.

Grading Basis: Letter Grade

Requires prerequisite courses of BANA 6610 and BANA 6620 (minimum grade C). Restricted to graduate majors and NDGR majors with a sub-plan of NBC within the Business School or Data Science and Computer Science graduate majors.

Optimization is a core component of Business Analytics, addressing decision problems that can be modeled and analyzed to identify optimal or near-optimal actions. This course develops the ability to translate complex business or engineering challenges into analytical models that guide data-driven and evidence-based decisions. While the focus of the course is on modeling and solving a wide variety of optimization problems, we’ll also cover the basic mathematical underpinnings of linear programming, along with emerging approaches that integrate artificial intelligence (AI). Students are exposed to introductory concepts in machine learning–assisted optimization and heuristic search, and learn how these approaches can support prescriptive analytics by assisting model formulation, informing adaptive decisions, and demonstrating simple intelligent automation in complex settings. In a limited, high-level role, Generative AI is used as a disciplined modeling assistant to help organize problem information, suggest candidate model elements for human review, and draft starter code or documentation that is verified against mathematical formulations and solver outputs. This structured use highlights human oversight, critical reasoning, and the responsible application of AI in analytics. Students will gain hands-on experience with state-of-the-art software for solving optimization and AI-enhanced decision problems. Team projects will apply these methods to meaningful, real-world business problems requiring analytical insight and computational skill.

Grading Basis: Letter Grade

Restriction: Restricted to BANA-MS students within the Business School.

This course shows how to apply causal modeling to develop robust, causally effective business policies and interventions; and quantify their impacts using realistically imperfect data under uncertain and changing conditions. Students create causal models of customer behaviors and responses to business initiatives; quantify lifts caused by campaigns; and design customer and employee policies and interventions with robust benefits despite real-world uncertainties and data limitations. Prior exposure to probability, statistics, optimization and R programming language is helpful but not essential. The course includes AI- and ML-assisted causal discovery, interpretation, and validation. Students learn to use large language models (LLMs) to assist causal interpretation and evidence synthesis, and to construct and analyze causal Bayesian networks representing business mechanisms and intervention effects. The course also covers causal trees, heterogeneous treatment effect (HTE) estimation, and the use of partial dependence plots to visualize causal relationships in predictive and prescriptive analytics. These tools provide a foundation for causal AI—the integration of causal reasoning into intelligent business systems and decision support.

Grading Basis: Letter Grade

Introduces the design, analysis, management, and control of supply chains. Because of continuing advances in globalization, sustainability, and information technology, course emphasis will include integration of processes and systems, relationship management of upstream and downstream players, and strategies that incorporate current and future trends. Cross-listed with INTB 6730.

Grading Basis: Letter Grade

Restriction: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School.

Typically Offered: Fall.

The course equips the Business Analyst with foundational concepts and techniques required for telling a compelling story with large complex data sets. The importance of visualizing information for many analysts is often overlooked or downgraded as a natural product of the analytics or model but if the visualization is ineffective the decision making processes and knowledge discovery will be compromised. This is a project-based course that begins with reviewing concepts of human perception and cognition and perceptual accuracy and preferences. In the weeks we have together we will explore the basics of graphic design and making a “good” graph, explore why some data visualizations present information effectively and others do not, and we will also consider visualization as a component of systems for the Data Scientist and Business Analyst and presents examples of EDA (exploratory data analysis), visualizing time, networks, and maps. We end by reviewing methods and tools for static and interactive graphics. Tableau or other cutting-edge software will be utilized.

Grading Basis: Letter Grade

Restricted to graduate majors and NDGR majors with a sub-plan of NBC within the Business School, and Data Science and Computer Science graduate majors.

Introduces principles of design of experiments (DOE), multivariate trials, randomized control trials (RCTs), A/B testing, and multi-armed bandit (MAB) optimization to evaluate and improve business processes, CRM and HR policies, and marketing campaign design and performance. Students learn to design evaluation studies and analyze data to critically evaluate and improve business process design and targeting, timing, content, context, and channel decisions to increase employee and customer satisfaction and long-term value (LTV). Prior exposure to probability, statistics, and R is helpful but not essential. This course introduces AI- and ML-assisted design of experiments, adaptive experimentation, and intelligent optimization for evaluative analytics. Students use AI/ML tools to automate experiment selection, analyze large-scale multi-factor data, and dynamically adjust interventions using contextual bandits and reinforcement learning. These AI-enhanced approaches improve the precision, efficiency, and scalability of modern evaluative and optimization strategies in business applications.

Grading Basis: Letter Grade

BANA 6780 introduces current artificial intelligence (AI) and machine learning (ML) technology, together with business use cases and AI/ML technology strategy for managers. Students learn how a variety of companies, from Netflix to electric utilities, apply modern AI/ML techniques to predict and manage customer demand, preferences, experiences, and behaviors; improve business processes and KPIs; automate and optimize routine business decisions; and develop more successful business strategies. Take-home software labs and LLM-assisted demos enable students to experiment with recommendation engines, Bayesian probabilistic inference systems, pattern recognition and predictive analytics, natural language processing (NLP), anomaly detection, causal inference, and optimization and coordination of plans and decisions over time and within teams and organizations of AI agents. Students apply these AI/ML techniques to business strategy and use cases and present their analyses in a written report.

Grading Basis: Letter Grade

Restricted to graduate majors and NDGR majors with a sub-plan of NBC within the Business School, and Data Science and Computer Science graduate majors.

Focuses on the design, operation, and strategic management of supply chains in a global context. Emphasizes the impact of globalization, international trade policies, cross-border logistics, and geopolitical risk on global supply networks. Topics include sourcing strategies, transportation and distribution systems, supplier relationship management, and performance measurement in multi-national supply chains. Case studies and real-world data are used to analyze challenges and opportunities in diverse international settings. Cross-listed with INTB 6790

Grading Basis: Letter Grade

Restriction: Restricted to graduate students and graduate non-degree seeking students.

Typically Offered: Fall.

A number of different current topics in business analytics are discussed in this course. Consult the current schedule for semester offerings. Repeatable.

Grading Basis: Letter Grade

Repeatable. Max Credits: 12.

Restriction: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School.

Introduces the fundamentals of artificial intelligence and its applications in business analytics. Students learn neural network principles, deep learning architectures, natural language processing, and generative AI, gaining hands-on experience with large language models and AI-driven tools for data processing, visualization, and decision support. Topics include prompt engineering, AI-powered automation, multi-agent systems, and strategies for integrating AI into analytics workflows to enhance efficiency and insights.

Grading Basis: Letter Grade

Requires prerequisite courses of BANA 6610 and BANA 6620 (all minimum grade C). Restricted to graduate majors and NDGR majors with a sub-plan of NBC within the Business School.

This course teaches students how to strategically integrate large language models (LLMs), generative AI tools, and agentic AI frameworks into the business analytics lifecycle, from data exploration and preparation to predictive analytics, machine learning models, and reporting. Students gain hands-on experience designing intelligent, adaptive workflows using LLMs and multi-agent systems. Key topics include prompt engineering, orchestration of multi-agent pipelines, and best practices for deploying AI-driven business analytics solutions. By the end of the course, students will design and implement a production-ready Generative AI application for a real-world business problem, demonstrating their ability to build enterprise-level AI solutions.

Grading Basis: Letter Grade

Requires prerequisite courses of BANA 6610 and BANA 6620 (all minimum grade C). Restricted to graduate majors and NDGR majors with a sub-plan of NBC within the Business School.

Instructor approval is required. Allowed only under special and unusual circumstances. Regularly scheduled courses cannot be taken as independent study. Repeatable.

Grading Basis: Letter Grade

Repeatable. Max Credits: 6.

Restriction: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School.

This is designed to be one of the final courses in the MS BANA degree. The Business Analytics Capstone serves as the culminating experience in a business analytics program, providing students with the opportunity to apply the knowledge, skills, and tools they have acquired throughout their coursework to real-world business challenges. This hands-on, project-based course is designed to bridge the gap between academic learning and professional practice, enabling students to demonstrate their ability to solve complex business problems using data-driven decision-making. Active discussion and creative presentation are core activities of this capstone course. Students will integrate what they have learned into a final project that can be either real-world problem designed in collaboration with an organization or a research paper on an emerging topic in the field. The final project will have multiple deliverables including a paper and a professional presentation to stakeholders who are directly related with the business problems defined in the project.

Grading Basis: Letter Grade

Restricted to graduate majors and NDGR majors with a sub-plan or NBA within the Business School, and Data Science graduate majors and Computer Science graduate majors.