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Designed as an introduction to Business Analytics, which considers the extensive use of data, methods and fact-based management to support and improve decision making. Business intelligence focuses on data handling, queries and reports to generate information associated with products, services and customers, business analytics uses data and models to explain business performance and how it can be improved. The class will be built on heavy hands-on coding; it will introduce and subsequently involve extensive use of Python.

Learn how to use AI as a tool for learning, doing stats and unlocking data insights in this course. The course will also show how AI can support analysis with problem solving, probability, distributions, statistical inference, regression analysis using relevant case studies.Ìý

This course will use platforms like Google Big Query, Tableau and Snowflake to learn entity relationship diagrams, relational schema mapping, SQL, normalization, data visualization and privacy/security.

Explores both the functional and technical environment for the creation, storage and use of the most prevalent source and type of data for business analysis, ERP and related structured data. Students will learn how to access and leverage information via SQL for analysis, aggregation to visualization, create dashboards, and be source for business intelligence.

This course introduced predictive analytics, supervised and unsupervised segmentation, discriminant functions, over fitting and evaluating and improving time series in machine learning.

This course exposes the students to commonly used platforms for statistical and predictive analytics. The class will go into depth of analytics using Python. Students will learn to analyze large datasets, including textual analytics such as twitter-stream analysis. The class will focus on predictive analytics.

Use AI-assisted coding methods while learning language chains, flow design, multimodal LLMs, reasoning, efficient tuning and tools for model deployment using team-based projects.

Learn how to leverage generative artificial intelligence (GAI) to solve business problems focusing on three key aspects of GAI for business: 1) use of open and closed large language models (LLMs) for coding; 2) models that focus on text as well as multimodal models incorporating text, vision, and/or audio; 3) sharing models with stakeholders through methods and platforms including GUIs, APIs, and local/cloud hosting options. Coding experience is not a prerequisite for this course.

MSBC 5680 Optimization Modeling

Using Python and Excel you will learn linear programming and how to evaluate for sensitivy analysis and applications. The course also covers how to solve optimization and network models and apply it to large-scale modeling.

Focuses on formulating decision problems as mathematical models and employing computational tools to solve them. Microsoft Excel is used as the main modeling platform but the course will also cover advanced tools, such as modeling languages. Optimization modeling will be illustrated in problems associated with operations, marketing, management, and finance. Integrates topics from decision analysis and operations management as they relate to modeling management decisions.

NURS 6286 Foundations of Healthcare Informatics

Use Open AI tools as you learn about the core concepts in healthcare including health IT for safety, quality, consumer-centered care and efficiency.

Students will develop the skillset needed to retrieve, analyze and interpret a vast range of health-related data. Meanwhile, students will learn about predictive analytics including operations research, aspects of computer science and statistical methods. Gain understanding of the informatics structure, organization and functioning of healthcare systems from the lens of invested parties, regulatory frameworks and healthcare delivery models. Learn about healthcare compliance regulations (e.g., HIPAA) and privacy requirements governing the use of healthcare data and develop proficiency for compliance in analytics projects.

MBAX 6331 Market Intelligence

Learn methods for quantitative and qualitative analysis, sampling, causality, regression, customer lifetime value (CLV), and model-driven analysis using R.

Market Intelligence is a marketing decision-oriented course geared toward gathering, analyzing, and interpreting data about markets and customers for both products and services. It is for managers as users of market information across marketing management, consulting, general management, and entrepreneurship to address problems of market selection, segmentation, positioning, new products, customer value and retention, pricing, communication, channel, etc.

MSBX 5417 Fundamentals of AI for Business

Learn about networks, sets, functions, logic, probability expectations, decision theory, correlation vs. causation optimization, vectors, matriices, finite state machines, game theory and feedback loops.

Using tools like R, Python and Big Query you will evaluate a full project lifecycle from understanding business and data, preparation, data cleanup, data modeling, evaluation, financial analysis and economic impact using data-driven insights and storytelling.

Provides an opportunity to execute a project for a company, integrating course work knowledge in an applied capstone experience. Allows first hand exposure to the business analytics as both an observer and creator of the business analytics process. Students work closely with an area client company to solve an important business analytics problem under the close supervision of the instructor.

This course dives into tools like Spark for streaming analysis, cloud computing with AWS, and managing databases and distributed computing with tools like Databricks.

Moves the student beyond structured data and sources into business scenarios where data is semi-structured to unstructured such as those from social and web applications. Specific topics include introduction to SQL-on-Hadoop, NoSQL and related distributed processing technologies. Students will learn practical application and mechanisms for getting this sort of data ready for analytics.

Learn an overview of Generative AI and LLMs used for deep learning and reinforcement learnings and how to interpret and explain AI, while diving into the ethical components of AI.

MBAX 6843 Supply Chain & Operations Analytics

Learn how to operationalize the supply chain including forecasting, sales, operating planning, waiting time analytics, inventory planning, supply contracts, risk pooling and strategic capacity management.

Analyzes key issues related to the design and management of operations and supply chains using quantitative tools such as linear, integer, and non-linear programming, regression, and statistical analysis. Covers important topics such as forecasting, aggregate planning, inventory theory, transportation, risk pooling, production control and scheduling, and facilities location, among others. Uses mathematical modeling, spreadsheet analysis, case studies, and pedagogical simulations to deliver material.

MBAX 6410 Process Analytics

This course identifies tools for process design to manage process flows, queuing systems, simulation modeling and optimization, culminating in a class project to demonstrate leaning outcomes.

Covers the concepts and tools to design and manage business processes. Emphasizes modeling and analysis, information technology support for process activities, and management of process flows. Graphical simulation software is used to create dynamic models of business processes and predict the effect of changes. Prepares students for a strong management or consulting career path in business processes.Ìý

NURS 6290 Information Systems Lifecycle

Get an intro to IS Dev including the planning, analysis, design, implementation and evaluation.

MSBC 5425 NLP for Healthcare Analytics

Learn NLP foundations, biomedical search engines, measuring population health, googling symptoms, doctor visit summaries, chatbot traige using Python.Ìý

MSBX 5310 Customer Analytics

Learn linear modeling, regression analysis, perceptual maps, cluster analysis, customer evaluation, retention and optimizing a marketing mix model.

Provides a deep understanding of how to use data on customer behavior and preferences to inform managerial decision making. Introduces methods for causal inference, modeling consumer demand, and modeling firm decisions. Applications include long-run customer management decisions (customer acquisition and retention) and short-run marketing mix (product, price, promotion and distribution) decisions. The R programming language is used for course examples and assignments. Students are assumed to have a working knowledge of R and linear regression techniques.

MSBX 5320 Digital Advertising

Learn a deep dive into digital advertising including digital campaigns, search, display, programmatic and how to read and interpret data in digital advertising.Ìý

Covers both traditional and emerging digital advertising methods, the popular platforms used to execute ads, and the leading analytic tools that can be used to assess advertising performance. Core advertising platforms covered include search, display, social media, native advertising, sponsored content and mobile. This class focuses on best practices and Key Performance Indicators that go with each advertising platform. Department consent required.

MSBX 5419 Agentic AI

Learn how to build agentic AIs from the ground up setting goals, intent, autonomy to develop the architecture, tools, prompt planning to build agentic AIs for business ops that are evaluated on the success and ethics in agents.

MBAX 6420 IT & Business Strategy

Make data-driven strategy decisions using business models and managing areas of digital transformation, infrastructure and and emerging technologies.Ìý

Although some companies are very successful in discovering and cultivating innovative technology-enabled business strategies, many fail in the process. Combines theories and frameworks with practical approaches to provide students with the skills required to help companies identify business opportunities, find appropriate information related technologies, and lead adoptions efforts to success.