Business Analytics

BUAN 6009 Business Analytics Internship (0 semester credit hours) Student gains experience and improves skills through appropriate developmental work assignments in a real business environment. Student must identify and submit specific business learning objectives at the beginning of the semester. The student must demonstrate exposure to the managerial perspective via involvement or observation. At semester end, student prepares an oral or poster presentation, or a written paper reflecting on the work experience. Student performance is evaluated by the work supervisor. Pass/Fail Only. Student cannot enroll in this course after completing BUAN 6390 or BUAN 6v98. Prerequisites: (MAS 6102 or MBA major) and department consent required. (0-0) S

BUAN 6311 (FTEC 6311) Robotics and Financial Technology (3 semester credit hours) This course focuses on different robotic technologies used in finance. (3-0) Y

BUAN 6312 (MECO 6312) Statistical Data Science for Business Decisions (3 semester credit hours) Provides hands-on training in statistical data-science methods for analyzing business and economic data. Build and interpret quantitative models for cross-sectional, time-series, and panel datasets. Key topics include causal inference with observational data, strategies for addressing endogeneity and unobserved heterogeneity, instrumental variables, Difference-in-Difference, panel-data, and time-series techniques. Emphasizes empirical applications and translating model results into evidence-based business and policy decisions. Credit cannot be received for more than one of the following: BUAN 6312 or FIN 6318 or MECO 6312. Prerequisite or Corequisite: OPRE 6301 or OPRE 6359 or BUAN 6359 or FIN 6306 or FIN 6307 or SYSM 6303. (3-0) S

BUAN 6319 (MIS 6359 and OPRE 6319) AI in Manufacturing (3 semester credit hours) Explores design and efficient operation of a variety of manufacturing system types, including fully automated and manual manufacturing, integrated circuit fabrication and assembly, and robotics systems. Recent as well as future sophisticated machine learning approaches, to design appropriate production systems and produce the desired products on these systems, are discussed. Examples of production systems from all over the world are presented to show the wide variety of system designs and problems. Strategic as well as economic justification issues are covered. Expert Guest Speakers and Plant Tours are scheduled. AI approaches in manufacturing can be further explored in Group Projects. (3-0) Y

BUAN 6320 (HMGT 6338 and SYSM 6338) Database Foundations for Business Analytics (3 semester credit hours) Covers Structured Query Language (SQL) and NoSQL databases and focuses on understanding the differences and learning how to query SQL and NoSQL databases effectively. Topics include ER models, SQL, mapping an ER model to a relational schema, NoSQL database types, and NoSQL querying. Credit cannot be received for more than one of the following: BUAN 6320 or HMGT 6338 or SYSM 6338 or MIS 6326 or ACCT 6320 or ACCT 6321 or MIS 6320 or OPRE 6393. (3-0) S

BUAN 6321 (ENGY 6320 and MECO 6321) Energy Analytics (3 semester credit hours) Applies quantitative methods to energy markets. Uses regression, time-series analysis, and machine learning to forecast electricity load, energy prices, and assess investment decisions across oil and gas, power, and transportation sectors. Applications address market design, reliability, resource depletion, and low-carbon technology integration. Case-based assignments develop skills for turning energy data into actionable insights for business and policy decisions. (3-0) F

BUAN 6324 (MIS 6324 and OPRE 6399) Business Analytics With SAS (3 semester credit hours) This course covers theories and applications of business analytics. The focus is on extracting business intelligence from firms' business data for various applications, including (but not limited to) customer segmentation, customer relationship management (CRM), personalization, online recommendation systems, web mining, and product assortment. The emphasis is placed on the 'know-how' -- knowing how to extract and apply business analytics to improve business decision-making. Students will also acquire hands-on experience with business analytics software in the form of SAS Enterprise Miner. Credit cannot be received for more than one of the following: BUAN 6324 or BUAN 6356 or MIS 6324 or OPRE 6399. Prerequisite or Corequisite: OPRE 6301 or OPRE 6359 or BUAN 6359. (3-0) Y

BUAN 6327 (ENTP 6383 and MIS 6397 and OPRE 6333 and SYSM 6330) IT and AI: Strategy and Innovation (3 semester credit hours) Addresses the business implications of recent advances in Artificial Intelligence (AI), Analytics, and Information Technology (IT). Uses lectures, article reviews, and real-world case studies to cover emerging business issues--ranging from AI, analytics, and cloud-based computing to cyber risk management, personalization, talent strategy, etc. Explores how these forces converge to enable digital transformation and provide opportunities for innovation and entrepreneurship. Group-based projects further allow exploration of implications for specific vertical sectors. (3-0) S

BUAN 6333 Foundations of Programming for Business Analytics (3 semester credit hours) This course will introduce students to two of the most popular and widely used languages in analytics: Python and R. Various packages, libraries, and functions associated with these languages will be covered in detail. The course is intended to prepare students for advanced courses that use one or both of these languages, i.e., the intended audience comprises students who want to gain a solid understanding of programming in the context of analytics. (3-0) S

BUAN 6335 (SYSM 6335) Organizing for Business Analytics Platforms (3 semester credit hours) The course develops conceptual understanding of platforms for business analytics and key business drivers that lead to business initiatives. The course examines how decision-makers in key functional areas of an enterprise rely on business analytics, how teams identify and develop analytical techniques to solve business problems, and how analytics platforms are adopted successfully. The course also emphasizes the development of business cases for strategic analytics initiatives and discusses best practices for descriptive, predictive, and prescriptive analytics. (3-0) S

BUAN 6336 (HMGT 6337 and MIS 6387 and SYSM 6347) Applied AI in a Healthcare Setting (3 semester credit hours) Considers Artificial Intelligence (AI) in a larger enterprise strategy, emphasis on AI as a tool to improve costs, access, patient impact, and unmet needs by extracting actionable intelligence and data from healthcare systems. Prerequisite: HMGT 6320. (3-0) S

BUAN 6337 (MKT 6337) Predictive Analytics for Data Science (3 semester credit hours) This course is designed to provide students with in-depth knowledge of the data-driven analytical techniques used in data-driven decision-making, especially in marketing. Students analyze data from real-world datasets to make managerial decisions. These Data-Science methods are commonly employed in online marketing, retail, and financial services. Students will acquire knowledge about the methods and software that are used to understand issues such as who the profitable segments/customers are, how to acquire them, and how to retain them. The tools can also be used to manage data-driven decisions from consumer purchase behavior to firms' policy settings such as pricing and branding. Prerequisite: OPRE 6301 or BUAN 6359 or OPRE 6359. (3-0) S

BUAN 6340 Programming for Data Science (3 semester credit hours) Covers many aspects of programming for data science and analytics, including syntax, handling data, data visualization, and implementation of statistical analysis models. Taught using Python language and may use a different programming language as applicable. Prerequisite: BUAN 6356 or BUAN 6383 or MIS 6386 or MIS 6334 or MIS 6356 or MIS 6382. (3-0) S

BUAN 6341 (MIS 6341 and OPRE 6343) Applied Machine Learning (3 semester credit hours) This course covers machine learning models for business data including text mining, natural language processing, non-linear regression models, resampling methods and advanced neural networks and artificial intelligence-based models for data-driven analytics. The course will be taught using either R or Python language. Prerequisites: (BUAN 6356 or BUAN 6383 or MIS 6386 or BUAN 6324 or MIS 6324 or OPRE 6399) and (OPRE 6359 or BUAN 6359). (3-0) S

BUAN 6342 Applied Natural Language Processing (3 semester credit hours) Focuses on extracting actionable insights from textual data. Utilizes rule-based and statistical methods for data analysis. Machine-learning programs to analyze and interpret human language. Applications: classical text, social media, business text/unstructured data. Prerequisite: BUAN 6341. (3-0) S

BUAN 6343 Applied AI (3 semester credit hours) Practical development of applications using modern artificial intelligence tools. Topics include machine learning, natural language processing, and large language models, with emphasis on foundation models such as language, vision-language, and multimodal systems. Hands-on experience with prompt engineering, retrieval-augmented generation, agent-based systems, and multimodal techniques through platforms such as OpenAI, Hugging Face, LangChain, and CrewAI. Design and implement applied AI solutions that enhance business processes and support decision-making. (3-0) S

BUAN 6344 (MIS 6344) Web Analytics (3 semester credit hours) The course examines the technologies, tools, and techniques to maximize return from web sites. The course includes topics related to web site design issues, web data collection tools and techniques, measurement and analysis of web traffic, visitor tracking, search engine optimization, visitor acquisition, conversion and retention, key performance indicators for web sites, and measurement of online marketing campaigns. The use of web analytics tools such as Google Analytics will be an integral part of the course. (3-0) S

BUAN 6346 (MIS 6346) Big Data (3 semester credit hours) This course explores big data analytics and cloud computing, focusing on practical applications. Students will be exposed to the uses of cloud platforms and distributed computing frameworks like Hadoop, Hive, Spark, and Databricks for processing large datasets. Through hands-on projects, they gain experience in setting up big data environments, designing scalable data pipelines, implementing real-time analytics, and optimizing queries. The course covers distributed analytics programming, applying machine learning and deep learning to big data, and addresses data governance, security, and cost management in cloud solutions. Students will use leading big-data tools to develop end-to-end analytics processes. Prerequisite: BUAN 6320 or MIS 6320 or MIS 6326. (3-0) S

BUAN 6347 Advanced Big Data Analytics (3 semester credit hours) The course covers Spark using Scala in a Hadoop environment. The topics include Scala syntax, Spark streaming, GraphX, MLlib, and other features of Spark. This advanced course requires students to have prior skills and working knowledge of big data environment and Python functional programming. Prerequisite: BUAN 6346. (3-0) T

BUAN 6356 (MIS 6356 and OPRE 6305) Business Analytics With R (3 semester credit hours) This course covers theories and applications of business analytics. The focus is on extracting business intelligence from firms' business data for various applications, including (but not limited to) customer segmentation, customer relationship management (CRM), personalization, online recommendation systems, web mining, and product assortment. The emphasis is placed on the 'know-how' -- knowing how to extract and apply business analytics to improve business decision-making. Students will also acquire hands-on experience with business analytics software in the form of R. Credit cannot be received for both courses, BUAN 6324 and BUAN 6356. Prerequisite or Corequisite: BUAN 6359 or OPRE 6359. (3-0) S

BUAN 6357 (MIS 6357) Advanced Business Analytics with R (3 semester credit hours) This course is based on the open-source R software. Topics include data manipulation, imputation, variable selection, as well as advanced analytic methods. Students will also learn various advanced business intelligence topics including business data analytics, modeling, customer analytics, web intelligence analytics, business performance analytics, and decision-making analytics. Tools to be used include R. Credit cannot be received for both courses, (MIS 6334 or OPRE 6334) and (BUAN 6357 or MIS 6357). Prerequisites: (BUAN 6356 or MIS 6356 or OPRE 6305) and (OPRE 6359 or BUAN 6359). (3-0) Y

BUAN 6358 (MIS 6347) AWS Cloud Analytics (3 semester credit hours) This course integrates data engineering, machine learning, and AI services to build enterprise analytics platforms. Students will create data pipelines for ingesting, storing, processing, and visualizing batch and streaming data. The course covers selecting and applying machine learning models, including foundation models and LLMs, to solve business problems. Through hands-on labs, students will model, build, train, and deploy custom ML models and AI algorithms. Aligned with AWS Data Engineering and Machine Learning certifications, this course prepares students to lead data-driven AI initiatives. (3-0) F

BUAN 6359 (OPRE 6359) Advanced Statistics for Data Science (3 semester credit hours) This course uses statistical methods to analyze data from observational studies and experimental designs to communicate results to a business audience. The course mandates prior knowledge of fundamental statistical concepts such as measures of central location, standard deviations, histograms, the normal and t-distributions (knowledge of calculus is not required). The course also emphasizes interpretation and inference, as well as computation using a statistical software package such as R or Python. Credit cannot be received for both: OPRE 6301 and (OPRE 6359 or BUAN 6359). (3-0) S

BUAN 6368 (MIS 6368) Applied Cybersecurity Analytics and Risk Management (3 semester credit hours) Students will explore IT Security and Analytics, perform hands-on exercises identifying security gaps with simulated data (application logs, network monitor logs, firewall logs, etc), and create predictions about potential security threats that could exploit the gaps. This course allows students to get an in-depth exposure to cybersecurity concepts and topics including security and risk management (legal, regulatory compliance), asset security (data classification, ownership, data security, and privacy), security engineering (security architecture, design, and security models), telecommunication and network security (perimeter protection, network attacks, IDS, IPS, firewalls), identity and access management (authentication, authorization, identity as a service), security assessment and testing, security operations (business continuity, disaster recovery, incident management, vulnerability and patch management), cryptography, and software development security. They will evaluate simulated data to identify security flaws and predict an organization's security position. (3-0) S

BUAN 6375 (ENTP 6375 and MKT 6375 and MIS 6375 and OPRE 6394 and SYSM 6332) Technology Strategies and New Product Development (3 semester credit hours) Addresses the strategic and organizational issues confronted by firms in technology-intensive environments and assess how successful firms manage these issues. Reflects six broad themes: (a) managing firms in technology-intensive industries; (b) forecasting key industry and technological trends; (c) linking technology and business strategies; (d) using technology as a source of competitive advantage. Uses lectures, article reviews and real-word case studies to cover the above topics. Group-based projects further allow exploration of implications for specific vertical sectors. (3-0) S

BUAN 6378 (ENTP 6388 and MIS 6377 and OPRE 6358 and SYSM 6316) Corporate Innovation and Entrepreneurship (3 semester credit hours) Importance of Innovation and Entrepreneurship in established corporations, how to assess Entrepreneurial Intensity, and how best to organize and structure new ventures. Characteristics of the entrepreneur, or entrepreneurs inside large companies, who combine innovation, creativity, and leadership to develop and launch new products, new product lines, and new businesses. Through lectures, article reviews and real-word case studies the course addresses (a) building blocks for corporate entrepreneurship, (b) creating the entrepreneurial organization, and (c) achieving and sustaining entrepreneurial performance. Group-based projects further allow exploration of implications for a specific large company. (3-0) Y

BUAN 6380 (ENTP 6390) Business Model Innovation (3 semester credit hours) A logical and internally consistent approach to the design and operations of a new venture, capturing the essence of how the business will be focused and providing a concise representation of how an interrelated set of decision variables will be addressed to create sustainable competitive advantage. Explores the range and diversity of existing business models and the analytical tools essential to their understanding, defines a logical and internally consistent approach to the choice or development of an appropriate business model for a new enterprise, and demonstrates the application of these tools and techniques through case studies and exercises. (3-0) Y

BUAN 6382 Applied Deep Learning (3 semester credit hours) Covers fundamentals of Deep Learning applied to business data. Includes various neural network architectures such as Feedforward Artificial Neural Networks, Convolutional Neural Networks, and Transformer Models for Computer Vision in different business scenarios. Prerequisite or Corequisite: BUAN 6341 or MIS 6341 or OPRE 6343. (3-0) S

BUAN 6383 (MIS 6386) Modeling for Business Analytics (3 semester credit hours) This is a fast-paced course that starts with an introduction covering popular approaches in business analytics (e.g., pre-processing, dimensionality reduction, association rules, clustering, basics of classification), proceeds into advanced methods (e.g., additional classification models, ensemble methods), and concludes with advanced models in customer analytics (e.g., discrete time models, continuous time models, count models, choice models). While the tool of choice will be Python, the focus of the course will be on modeling (i.e., this is not a course intended to teach you Python) - familiarity with Python is assumed. Credit cannot be received for both courses, (MIS 6334 or OPRE 6334 or BUAN 6357 or MIS 6357) and (BUAN 6383 or MIS 6386). Prerequisite or Corequisite: BUAN 6359 or OPRE 6359. (3-0) S

BUAN 6384 Sustainability Analytics (3 semester credit hours) The course covers data engineering, modeling techniques, KPI grooming, and analysis of outcomes related to sustainability and ESG. It explores qualitative and quantitative aspects of sustainability within the UN SDGs framework. Students assess business practices and performance on sustainability issues and use case studies to predict and prescribe solutions with various analytical techniques. Additionally, the course involves designing systems to transform raw data into actionable insights, aiding organizations in achieving their sustainability goals. (3-0) Y

BUAN 6385 (MIS 6385 and SYSM 6339) Robotic Process Automation (3 semester credit hours) This course is intended to provide students with practical literacy on robotic process automation through real-world, relevant data preparation use cases. It will help identify potential uses and the benefits and considerations for robotic process automation. The students will learn the elements of a business process and the basics of developing a BPM application, implementing triggers to automate processes, defining and measuring KPIs. Students will use elements of artificial intelligence (AI) and machine learning capabilities to handle high-volume, repeatable tasks that previously required humans to perform. These tasks can include queries, calculations, and maintenance of records and transactions. Students will be able to use apply analytics to the generated data for a systematic computational analysis of data for the discovery, interpretation, and communication of meaningful patterns in data that will be used towards effective decision making. (3-0) S

BUAN 6386 (MIS 6371 and OPRE 6322) SAP Cloud Analytics (3 semester credit hours) In the evolving field of Artificial Intelligence, mastering cloud analytics is essential. Covers cloud computing, data management, big data analytics, machine learning, AI integration, security, and compliance. Hands-on experience offered with tools like SAP BTP, Google Cloud Analytics, and Jupyter Notebooks. Exposure to cloud platforms, managing large datasets, deploying machine learning models, integrating AI solutions, and ensuring data security. Ideal for data scientists, AI practitioners, and IT professionals, the course recommends basic knowledge of AI, machine learning, and cloud computing. (3-0) F

BUAN 6388 (ENTP 6398 and MIS 6388 and SYSM 6315) The AI Corporate Entrepreneurial Experience (3 semester credit hours) Organizes in teams to engage with corporate mentors to identify unmet business opportunities satisfied through AI and technology-based business solutions. Collaborating with mentors, teams create value propositions through various stages of business plan development during the semester. Fine-tuning value propositions for - customers, end-users, value-chain partners, estimating market size, competitive strategy, business models, revenue potential, assessing operations issues, cost models, developing a go-to-market and sales plan, and creating a financial plan with ROI and breakeven analysis. Develops business plan presentation and writing skills aimed at executives and investors. (3-0) R

BUAN 6390 Analytics Practicum (3 semester credit hours) Student gains experience and improves analytics skills through appropriate developmental work assignments in a real business environment. Students must identify and submit specific business learning objectives at the beginning of the semester. Students must demonstrate exposure to the managerial perspective via involvement or observation. At semester end, the student prepares an oral or poster presentation or a written paper reflecting on the work experience. Students should have completed at least 18 credits in the MS Business Analytics program before enrolling in the course. Prerequisite: MAS 6102 or MBA major required. (3-0) S

BUAN 6392 (MIS 6392) Causal Analytics and A/B Testing (3 semester credit hours) This course teaches the critical distinction between correlation and causation in data, essential for managers to understand the impact of interventions. It covers designing and analyzing A/B tests to differentiate correlation from causation and using statistical techniques with observational data for reliable causal inferences. Through lectures, cases, and in-class exercises, students will work with simulated and real-world datasets, gaining practical tools for immediate application. Ideal for understanding the effectiveness of marketing strategies and promotions, this hands-on course equips students to implement successful interventions in various contexts. Prerequisite: OPRE 6301 or OPRE 6359 or BUAN 6359. (3-0) S

BUAN 6393 Advanced Database with AI Analytics (3 semester credit hours) Comprehensive exploration of database management through Python; progression from relational foundations to advanced topics including NoSQL, distributed databases, and optimization; integration of Artificial Intelligence with databases - vector search, embeddings, natural language querying; emphasis on practical coding and conceptual understanding for designing, optimizing, and building intelligent data-driven applications. Prerequisite: MIS 6320 or MIS 6326 or BUAN 6320 (3-0) S

BUAN 6394 (MIS 6391) Responsible AI and Ethical Governance (3 semester credit hours) Examination of ethical, societal, and governance challenges in the development and deployment of AI in business; exploration of ethical frameworks, philosophical traditions, and contemporary thought leadership; evaluation of AI implications on privacy, explicability, accountability, and human autonomy; application of case studies, policy analysis, and interdisciplinary dialogue to build analytical tools and leadership mindset for responsible AI innovation and governance in complex, real-world contexts. (3-0) S

BUAN 6396 (HMGT 6374 and MIS 6374 and OPRE 6324) Internet of Things (3 semester credit hours) Cornerstone of digital transformation and Industry 4.0, connecting billions of devices and transforming business operations. This course provides a competitive advantage by integrating current knowledge and hands-on skills in IoT, digital transformation, and emerging technologies. It is particularly relevant for professionals in Supply Chain, Healthcare, and Information Systems. Key topics include IoT components, communication technologies, security, cloud platforms, and data analytics. Learners will apply design thinking and process modeling to real-world use cases such as predictive maintenance and smart healthcare, fostering innovation, efficiency, and digital leadership. (3-0) Y

BUAN 6397 (HMGT 6334 and MIS 6305) Healthcare AI and Decision Analytics (3 semester credit hours) Using AI as an enabler, introduces the use of business intelligence and decision sciences in the healthcare industry to improve care, reduce costs, and provide more accessible care. Introduces the use of business intelligence, artificial intelligence, and decision analytics in the healthcare industry. Acclimates to data mining, machine learning, and decision analysis, as well as hands-on experience with analytics software and programming tools, which may include coding in R software (depending on availability). Prerequisite or Corequisite: BUAN 6359 or OPRE 6359. (3-0) F

BUAN 6398 (OPRE 6398) Prescriptive Analytics (3 semester credit hours) Introduction to decision analysis and optimization techniques. Topics include linear programming, decision analysis, integer programming, and other optimization models. Applications of these models to business problems will be emphasized. Prerequisite: OPRE 6301 or OPRE 6359 or BUAN 6359. (3-0) S

BUAN 6399 (HMGT 6399 and MIS 6399 and SYSM 6399) AI in Business (3 semester credit hours) Provides a broad overview of major AI (including Generative AI) and applications in business, balancing theory with practical hands-on skills. Presents core AI concepts around Machine Learning, Generative AI, Prompt Design, and business aspects of leveraging and deploying AI. Major AI use-cases in multiple domains, such as Sales and Marketing, Healthcare, Supply Chain, Operations Management, Finance, Software Development, Sports and Media, Human Resource Management, etc., are also covered. (3-0) S

BUAN 6V90 Individual Study in Business Analytics (1-3 semester credit hours) May be an individualized study for students pursuing further study of a topic in Business Analytics. Pass/Fail only. May be repeated for credit as topics vary (3 semester credit hours maximum). Additional prerequisites may be required depending on the specific course topic. Department consent is required. ([1-3]-0) S

BUAN 6V98 Business Analytics Internship (1-3 semester credit hours) Student gains experience and improves skills through appropriate developmental work assignments in a real business environment. Student must identify and submit specific business learning objectives at the beginning of the semester. The student must demonstrate exposure to the managerial perspective via involvement or observation. At semester end, student prepares an oral or poster presentation, or a written paper reflecting on the work experience. Student performance is evaluated by the work supervisor. Pass/Fail only. May be repeated for credit as topics vary (3 semester credit hours maximum). Prerequisites: (MAS 6102 or MBA major) and department consent required. ([1-3]-0) S

BUAN 6V99 Special Topics in Business Analytics (1-6 semester credit hours) May be repeated for credit as topics vary (6 semester credit hours maximum). Additional prerequisites may be required depending on the specific course topic. Instructor consent required. ([1-6]-0) Y