Business Analytics

BUAN 3301 AI in Business (3 semester credit hours) Fundamental concepts and applications of Artificial Intelligence (AI) in the business world, covering how AI technologies can be used to solve business problems, enhance decision-making, and drive innovation. Addresses ethical considerations and the impact of AI on various business functions, and the primary use cases of AI in Business, such as Sales and Marketing, Finance, Supply Chain, Healthcare, and Information Systems. Prerequisite: MATH 1325. (3-0) S

BUAN 4090 Business Analytics Internship (0 semester credit hours) Supervised work experience in a business analytics management setting; application of business concepts in a professional environment; evaluation of performance by work supervisor. Credit/No Credit only. May be repeated for credit as internships differ. Department consent required. (0-0) S

BUAN 4301 Cybersecurity Ethics, Privacy, and Compliance (3 semester credit hours) Exploration of ethical, legal, and regulatory issues in cybersecurity; data privacy, surveillance, and intellectual property; ethical dilemmas in monitoring, artificial intelligence, and technology use; governance structures ensuring accountability and oversight; legal frameworks including General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA); industry-specific compliance requirements; case studies of ethical breaches, regulatory violations, and controversies surrounding misuse of information. (Same as CYBR 4310) (3-0) T

BUAN 4320 Database Fundamentals for Analytics (3 semester credit hours) Foundations of relational database design and management with coverage of entity-relationship modeling, logical database structures, and data administration. Emphasis on Structured Query Language for data definition, manipulation, and optimization. Exploration of advanced database processing, including triggers and stored procedures. Introduction to NoSQL database models, types, and querying techniques, highlighting contrasts with relational approaches. Prerequisites: ITSS 3300 and ITSS 3311 and (MATH 1325 or MATH 2413 or MATH 2417) and (CS 2305 or MATH 2418 or MATH 2333 or OPRE 3333). (3-0) S

BUAN 4321 Business Data Engineering (3 semester credit hours) Architecture and orchestration of data pipelines for cleansing, wrangling, transforming, and integrating complex structured and unstructured datasets to support analytics, artificial intelligence, and machine learning. Emphasis on data quality, scalability, and efficiency through distributed processing and automation. Exploration of open-source frameworks such as Python (Pandas, PySpark), Apache Airflow, and OpenRefine, combined with cloud-native platforms including AWS, Azure, and Google Cloud for large-scale storage, transformation, and pipeline management. Focus on preparing enterprise-grade data for advanced analytical and AI applications in business contexts. Prerequisite: BUAN 4320 or ITSS 4300. Corequisite: BUAN 4381 or ITSS 4381. (3-0) Y

BUAN 4322 Causal Analysis and A/B Testing (3 semester credit hours) Principles and practices of causal analysis and A/B testing for data-driven business decisions. Covers foundational statistical concepts for designing experiments, interpreting results, and evaluating causal relationships using a causal inference framework. Includes methods such as backdoor adjustment and do-calculus at an introductory level. Emphasizes A/B testing in product development and user experience. Features hands-on use of Python for causal analysis and experiment design. Focuses on evaluating causal claims and applying modern tools to guide strategic business decisions. Prerequisite: BUAN 4373 and (BUAN 4381 or ITSS 4381). (3-0) Y

BUAN 4323 Applied Generative AI for Business (3 semester credit hours) Explores foundations and applications of large language models (LLMs) in business contexts. Covers core concepts in machine learning, natural language processing, and transformer-based architectures. Topics include tokenization, embeddings, sequence models, attention mechanisms, fine-tuning, prompt engineering, retrieval-augmented generation (RAG), workflow automation, and AI agent design. Examines multimodal systems, evaluation methods, and deployment strategies with open-source and commercial platforms. Emphasis on practical use of LLMs for business process optimization, decision support, and intelligent application development. Prerequisite: BUAN 4381 or ITSS 4381. (3-0) Y

BUAN 4328 Harnessing AI for a Sustainable Future (3 semester credit hours) Explores the intersection of AI and sustainability, highlighting both opportunities and challenges. Examines AI applications across sectors like Supply chain, human resources, energy, transportation, agriculture, health, water, logistics, and waste management to assess their impact on sustainable development goals (SDGs). Business case studies illustrate real-world use. Also addresses ethical and environmental risks associated with AI and concludes by exploring its influence on the future of work and smart cities. (3-0) S

BUAN 4337 Marketing Analytics (3 semester credit hours) This course is designed for those interested in an entry-level marketing analytics position. Students will analyze data to make key marketing decisions such as which customers to target to increase profitability or which new products to introduce to build incremental business. Students will also be introduced to software products used in the analysis of sales, marketing, and distribution data. Prerequisite: MKT 3300. (Same as MKT 4337 and OPRE 4337) (3-0) S

BUAN 4350 Spreadsheet Modeling and Analytics with AI (3 semester credit hours) Explores the use of quantitative methods and spreadsheet software enhanced with AI techniques and their applications. The approach emphasizes building effective analytical models to support decision-making in areas such as finance and operations. Gain knowledge of specific modeling techniques used to analyze data, evaluate alternatives, and support sound business decisions. Prerequisite: OPRE 3310. (Same as OPRE 4350) (3-0) S

BUAN 4351 Foundations of Business Intelligence (3 semester credit hours) Students are introduced to foundational business intelligence (BI) concepts and explore the theory and practice of data warehouses for enterprises. BI concepts including data mart schemas, ETL, OLAP, cubes, and reporting will be covered. The course will also examine the components of an enterprise data warehouse, extract, cleanse, consolidate, and transform heterogeneous data into a single enterprise data warehouse, and run queries using a data warehouse. Prerequisites: ITSS 3300 and (BUAN 4320 or ITSS 4300) and (MATH 1325 or MATH 2413 or MATH 2417) and (CS 2305 or MATH 2418 or MATH 2333 or OPRE 3333). (Same as ITSS 4351) (3-0) S

BUAN 4352 Introduction to Web Analytics (3 semester credit hours) Introduces technologies and tools used to realize the full potential of websites. The course focuses on the collection and use of web data such as web traffic and visitor information to design websites that will enable firms to acquire, convert, and retain customers. Online advertising such as paid search and web analytics tools will also be included. Prerequisites: ITSS 3300 and (MATH 1325 or MATH 2413 or MATH 2417) and (CS 2305 or MATH 2418 or MATH 2333 or OPRE 3333) (Same as ITSS 4352) (3-0) S

BUAN 4353 Business Analytics (3 semester credit hours) Explore the fundamentals of business analytics, focusing on data-driven decision-making. Topics include understanding of types of analytics, including descriptive, predictive, prescriptive, and diagnostic analytics. Focus will be on essential topics such as data collection, analysis, visualization, and interpretation. Supervised and unsupervised data mining techniques will also be explored. Applications of these methods to real-world business scenarios are covered. Gain hands-on experience with industry-standard software such as MS Excel, PowerBI and Python to enhance your analytical skills and strategic thinking. Prerequisites: (ITSS 3312 or OPRE 3312 or BUAN 4381 or ITSS 4381) and (MATH 1325 or MATH 2413 or MATH 2417) and (CS 2305 or MATH 2418 or MATH 2333 or OPRE 3333) and OPRE 3360. (Same as ITSS 4353 and OPRE 4353) (3-0) S

BUAN 4354 Advanced Big Data Analytics (3 semester credit hours) Advanced topics in supervised and unsupervised machine learning techniques using big data solutions such as Hive and Spark. Students explore the issues and challenges related to managing data within an organization. This course is designed to equip students with skills to address the business intelligence, data analysis, and data management needs of an organization. Students are introduced to machine learning techniques and big data technologies. Prerequisites: (ITSS 3312 or BUAN 4381 or ITSS 4381) and (BUAN 4320 or ITSS 4300) and (BUAN 4351 or ITSS 4351). (Same as ITSS 4354) (3-0) S

BUAN 4355 Data Visualization (3 semester credit hours) Focus on data visualization techniques for analyzing and communicating information in business contexts. Coverage includes design principles, data modeling for effective visuals, and practices of data storytelling. Development of dashboards for monitoring performance metrics and decision-making using Tableau, MS Excel, Power BI, and Python. Attention given to visual perception, color theory, and advanced visualization methods, combining analytical and narrative approaches to data communication. Prerequisites: (ITSS 3312 or BUAN 4381 or ITSS 4381) and (BUAN 4320 or ITSS 4300) and (BUAN 4351 or ITSS 4351). (Same as ITSS 4355) (3-0) Y

BUAN 4357 Supply Chain Analytics, AI, and Advanced Solutions (3 semester credit hours) This hands-on course equips students with the knowledge and skills to leverage analytical tools and programming languages such as Excel and Python for the practical application of analytical techniques within the realm of supply chain management. Topics covered include demand planning, forecasting, inventory and production optimization, transportation, and sales analysis. Students gain proficiency in using these analytical tools to address real-world challenges within complex supply chain systems. The course also delves into various AI use cases within supply chain operations, highlighting the role of AI-Led Analytics in optimizing supply chain management flows. Prerequisite: OPRE 3360 or STAT 3360. (Same as OPRE 4357) (3-0) Y

BUAN 4358 Responsible AI Practices in Business (3 semester credit hours) Illuminates challenges involved in the creation, deployment, and use of artificial intelligence (AI) in Business Operations. Presents frameworks and principles from great thinkers and leaders from the past as touchpoints to evaluate issues related to AI in Business. Presents guidance for the responsible implementation of AI for the long-term good of business and industry success. (3-0) Y

BUAN 4373 Data Science for Business Applications (3 semester credit hours) This course builds on the foundations of Probability and Statistics from OPRE 3360. It further develops knowledge and skills for applying statistical and management science models to business decision-making. Topics include hypothesis testing for several populations, intermediate multiple linear regression, variable transformation, model selection procedures, chi-square tests and contingency tables, design of experiments and Analysis of Variance (ANOVA), logistic regression, and non-parametric methods. The course uses statistical software. Prerequisite: OPRE 3360. (Same as OPRE 4373) (3-0) S

BUAN 4381 Object Oriented Programming with Python (3 semester credit hours) Explores fundamental concepts of Object-Oriented Programming (OOP) and applying principles using Python. Key OOP topics include classes, objects, inheritance, encapsulation, and polymorphism, as well as programming fundamentals for data science and analytics. Focus will be to integrate hands-on coding exercises to reinforce language constructs and utilize functions from basic libraries, along with practicing coding, including opportunities for collaboration through paired programming activities. Prerequisites: ITSS 3311 and (MATH 1325 or MATH 2413 or MATH 2417) and (CS 2305 or MATH 2333 or MATH 2418 or OPRE 3333). (Same as ITSS 4381) (3-0) S

BUAN 4382 Applied Artificial Intelligence/Machine Learning (3 semester credit hours) Broad exploration of machine learning, data mining, and pattern recognition in business contexts. Coverage of supervised and unsupervised learning methods, text analytics, and applications of AI techniques for business decision-making. Attention to data preparation, model selection, and evaluation. Emphasis on business framing, strategy, and return on investment, along with ethical and regulatory considerations in AI. Content delivered through lectures, discussions, labs, and case studies, focusing on practical methods and conceptual understanding across real-world applications. Prerequisite: ITSS 3312 or BUAN 4381 or ITSS 4381. (Same as ITSS 4382) (3-0) S

BUAN 4383 Machine Learning for Business Analytics (3 semester credit hours) Exploration of advanced applications of artificial intelligence and machine learning in business and analytics using Python. Coverage includes supervised and unsupervised methods, neural networks, deep learning, natural language processing, large language models, and reinforcement learning. Emphasis placed on data preparation, statistical foundations, model building, and evaluation techniques. Case studies and applied exercises highlight the role of AI in strategic decision-making, implementation, and governance. Ethical and regulatory considerations are integrated throughout, examining fairness, bias, and responsible deployment of AI systems. Prerequisites: (ITSS 3312 or BUAN 4381 or ITSS 4381) and (BUAN 4373 or OPRE 4373). (Same as ITSS 4383) (3-0) S

BUAN 4395 Capstone Senior Project - Business Analytics (3 semester credit hours) This course is intended to complement theory and provide an in-depth, hands-on experience in all aspects of a real analytics business project. Students will work in teams as consultants on projects of interest to the industry and will be involved in specifying the problem and its solution, designing and analyzing the solution, and developing recommended solutions. The deliverables will include reports that document these steps as well as a final project report, including the challenges faced by the team. Teams will also make presentations. Student groups will apply business analytics concepts and techniques in developing the report. Prerequisites: (BUAN 4320 or ITSS 4300) and (BUAN 4373 or OPRE 4373) and (BUAN 4381 or ITSS 4381). Prerequisite or Corequisite: (BUAN 4355 or ITSS 4355). (3-0) S

BUAN 4V81 Individual Study in Business Analytics and AI (1-3 semester credit hours) Credit/No Credit only. May be repeated for credit as topics vary (9 semester credit hours maximum). Instructor consent required. ([1-3]-0) R

BUAN 4V90 BUAN Internship (1-3 semester credit hours) Designed to further develop a student's knowledge of Business Analytics and AI through appropriate developmental work experiences in a true organizational setting. Students are required to identify and submit specific business learning objectives (goals) at the beginning of the semester. Student performance is evaluated by the work supervisor. Credit/No Credit only. May be repeated for credit (3 semester credit hours maximum). Department consent required. ([1-3]-0) S

BUAN 4V95 Seminar Series in Business Analytics and AI (1-3 semester credit hours) Discussion of selected topics and theories in Business Analytics and AI. May be repeated for credit as topics vary (9 semester credit hours maximum). Instructor consent required. ([1-3]-0) S