15 courses with the subject BUAG, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
BUAG 510Found Stats with R for BUAN3
This advanced course equips graduate students with cutting-edge statistical methods and predictive modeling techniques used in modern business environments. Emphasis will be placed on advanced regression techniques, time series forecasting, ANOVA, experimental design, and multivariate analysis. Students will apply these techniques using R, with a strong focus on real-world business applications and data-driven decision-making. Graduate-level rigor will require additional programming exercises, research, and complex data analysis projects.
Accelerated introduction to computer programming and the software tools used for data analysis and processing. Topics include Python programming, source code control, and command-line tools for data transformation and searching. The first week of the course will feature a quick review of spreadsheets with a focus on data validation and aggregate functions. Python programming related topics include basic programming constructs, data formats (csv, json), data acquisition (web scraping, web APIs) and libraries for data manipulation/analysis/visualization.
This course examines foundational privacy concepts and provides an overview of the legal and regulatory frameworks for current privacy laws. It explores sector-specific legislation in the U.S. and examines the differing legal requirements for handling and transferring data under the General Data Protection Regulation (GDPR). The course examines appropriate data governance practices and discusses how organizations devise privacy and data protection strategies. The course will use real-life case studies to examine the sufficiency of data protection measures used by organizations and will explore the potential liability and reporting requirements following a data breach. Students will learn consistent methods for applying privacy principles to emerging technologies in the areas of biometric data and artificial intelligence.
BUAG 610Data Mining Methods for Business Analytics3
In this course, students learn how to make sense out of data (Gather, Ingest, Store, Analyze, visualize train-models and Mine) for the purpose of decision support. Topics covered: Data Gathering, Repurposing Data, Descriptive Analytics, Outlier(s) Detection, Similarity Measures and Cluster Analysis, Classification & Prediction (Entropy, SVM, Naïve Bayes), Association Rules, Regression Analysis, Time Series Forecasting, Social Media Analytics and Text Mining. Generative AI Architecture(s) and platforms will also be introduced.
This course focuses on advanced statistical modeling techniques and their applications in business analytics. The curriculum emphasizes time series analysis, experimental design, advanced regression methods, and multivariate analysis. Students will use R and Python for real-world business problem-solving, with an emphasis on predictive modeling and the automation of data processes. The course concludes with a capstone project, providing hands-on experience with business case analysis and advanced forecasting techniques. Prerequisite: BUAN 510.
Data analytics have transformed how business executives make strategic decisions. Decision-makers now rely heavily on sophisticated quantitative analysis using the user-friendly environment of spreadsheet software, such as Microsoft Excel. This course introduces students to quantitative modeling for strategic decision-making across various business areas, including finance, marketing, and operations. Topics include decision-making under uncertainty, revenue management, simulation, and mathematical programming. While Excel will be used, the course focuses on understanding, structuring, and solving business problems and interpreting and communicating the solutions effectively in a realistic business context.
This is a Data Engineering Course. It covers the fundamentals of sound database design, storing, manipulating and retrieving data, Conceptual Data Modeling, SQL, Functional Dependencies and Data Normalization, transaction management, transferring data between Excel and csv and SQL-engines. JSON formats as well as NoSQL Concepts.
BUAG 627Advanced Machine Learning and Artificial Intelligence for Business Analytics3
This advanced graduate course provides a rigorous and in-depth exploration of machine learning (ML) and artificial intelligence (AI) frameworks for solving complex business problems. Building on foundational knowledge, students will engage with advanced ML algorithms such as neural networks, k-Nearest Neighbors (kNN), logistic regression, and Support Vector Machines (SVM), as well as state-of-the-art deep learning architectures like Convolutional and Recurrent Neural Networks (CNNs and RNNs). The course emphasizes ensemble methods, optimization techniques for large-scale data, and ethical considerations in AI, including bias and interpretability. Graduate students will be required to design and implement innovative, data-driven solutions to complex, real-world business challenges, enhancing both theoretical knowledge and practical expertise in AI/ML technologies. Prerequisite: BUAG 610.
This course links a sociological understanding of social network theory and analysis with the technical skills necessary to map and analyze these social media networks through data generated by social media. The course will emphasize social network theories and concepts as it applies to social media in support of team project development and executing using the technical skills learned over the course of the semester. The course utilizes modern computing tools and network-graphs theory to analyze social networks, their organization and communication. Prerequisite: BUAG 610.
This course covers key topics such as demand forecasting, inventory management, logistics optimization, and the use of big data in the supply chain environment. Students will learn to apply statistical methods and predictive models to real-world supply chain problems, enhancing efficiency and reducing costs. Through case studies and hands-on projects, the course also emphasizes the importance of sustainability and ethical considerations in supply chain decisions, preparing students to become leaders in enhancing supply chain operations within diverse industries.
BUAG 646Project Management for Business Analytics3
This course covers the application and integration of the project management body of knowledge (PMBOK) areas to managing projects. It focuses on project management tools and techniques for defining and managing the project’s goal, scope, schedule, and budget. Other topics include project selection, project management skills, organizational structures, and risk management. Projects are used in most industries and types of organizations to achieve unique outcomes given limited time and budget. The ability to select, initiate, plan and execute projects successfully is critical to any organization’s ability to achieve its goals. Managers are often exposed to projects in different roles including but not limited to sponsors, customers, users/recipients of benefits, project managers, program managers, resource managers, product managers, and funding approvers. The ability to understand how projects are managed is an important skill for managers to succeed in their jobs. This course provides students with this knowledge and gives them the foundation to be able to start a career as project managers. At the end of this course, students should be able to create a project plan and execute it to achieve pre-defined business objectives.
BUAG 660Introduction to Blockchains, Cryptocurrencies and Smart Contracts3
This course provides an in-depth overview of blockchains, cryptocurrencies and smart contracts, as well as their uses, advantages, and limitations. Blockchain is a distributed, immutable ledger technology that is utilized to implement cryptocurrencies, smart contracts and other applications where public or semi-private visibility and verifiability are required. In addition to Blockchain architecture and use, the course will also cover the necessary computing and cryptographic background needed to understand and utilize Bitcoin and smart contracts. For both technical and business professionals, it is essential to understand these technologies, as well as their associated security issues. Prerequisite: BUAG 605.
BUAG 667Generative AI and Deep Learning for Business Analytics3
This graduate-level course focuses on the theory, implementation, and business applications of generative AI and deep learning. Topics include deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), along with the business uses of models like GPT and BERT. The course emphasizes real-world applications, including image recognition, natural language processing (NLP), and predictive modeling. Students will also explore ethical concerns surrounding AI models, including bias and transparency, and will implement projects using Python and TensorFlow. Prerequisite: BUAG 627.
This course provides students with an opportunity to apply the knowledge and skills, acquired throughout the Business Analytics program, to a comprehensive, real-world project. Working individually or in teams, students will partner with industry clients or work on faculty-suggested projects to address meaningful business problems using data analytics. Emphasis is placed on project planning, data sourcing and wrangling, analysis, and the presentation of actionable insights. Students will produce a final written report and deliver an executive-level presentation. Projects may involve predictive modeling, optimization, machine learning, business intelligence, and data visualization. Prerequisites: BUAG 610.
