Curriculum

This course introduces the field of AI to business students with a particular emphasis on deep learning, which is driving the current AI revolution. The course consists of three modules:
1.    Foundations of AI and Deep Learning – Establishes the foundation of AI and deep learning.
2.    Neural Network Architectures – Introduces major neural network architectures widely used in practice.
3.    Generative AI – Touches on generative AI, which is particularly promising in recent years.
The course emphasizes experiential learning and contains many hands-on projects using TensorFlow broadly and Keras in particular.

In CIS455, students will engage in an AI-focused business project, bringing together the program’s conceptual frameworks, analytical methodologies, and technical skills. Students will work on real world projects that sponsoring organizations will provide. Student project teams will work with faculty coaches and any other relevant technical experts to complete their work for presentation and delivery to the client at the end of the course. These projects will require and further develop the AI, analytics, communication, and leadership skills learned throughout the program.

Companies and organizations in every industry rely heavily on their data, not only for the day to day tactical needs of running the business but also to generate information and analysis to support problem-solving and decision-making in a wide variety of applications. Database design and management along with systems techniques for data retrieval, transformation and analysis have the potential to play a key role in business strategy and to create business value and competitive advantage. CIS 467 builds an understanding of system data design to enable analytics as well as the concepts and tools necessary to leverage system data to meet an organization’s analytics needs. The course covers MySQL programming skills for data retrieval, including writing and executing queries; updating, inserting and deleting database records; creating views; and writing user-defined functions and stored procedures. CIS 467 also introduces a conceptual framework for analytics activities surrounding data warehousing, including creating ETL code to transform and transport data from a transitional database into a data warehouse for further analysis. Finally, students will use Tableau to design and create data visualizations and dashboards, both for exploratory data analysis and effective communication.

The course will introduce and apply frameworks related to answering these questions. The course will use a series of real-world cases and examples involving managerial decisions to connect analytics to real world problems. These cases will take the students through an active learning process that builds on and extends the skills and knowledge gained in previous courses. The course focuses heavily on “soft” skills related to structuring complex problems, decision-making with imperfect information, and designing analysis to address analytics research questions. The course also reinforces and expands on “hard” skills related to data wrangling, programming, descriptive, predictive, and causal analytics, as well as introducing some unsupervised learning methods. The course will leverage software tools such as sql, Python, and Tableau.

This course equips MS students with statistical skills necessary for data-driven decision making. The course covers central tendency and variability, probability, binomial and normal distributions, standard scores, hypothesis testing, z and t tests, ANOVA, correlation and regression, and non-parametric tests.

This course introduces students to the basics of economics and marketing strategy through interactive lectures and case discussions. Consumer choice, demand curves, the impact of competition and costs form the nucleus of the economics topics. Marketing strategy builds on these consumer, competition and company considerations to understand the segmentation, targeting, positioning and promotional decisions of the firm.

 This course provides a foundation in programming within the Python environment. Traditional programming concepts (operators, data structures, control structures, repetition, and user-defined functions) will be central to the learning objectives, but the concepts will be taught in the context of marketing and business analytics problems related to data management and visualization. In addition to high-level programming, the students will gain a foundational understanding of how data are organized and pulled from databases, including the querying process that turns raw data into the kinds of datasets that more advanced analytics tools leverage. The course involves hands-on tutorial assignments with practical pattern matching as well as less structured programming assignments, where the students are expected to write their own programs.

GBA478 covers the application of generative AI technologies across diverse business contexts. The course will help you understand how to integrate Generative AI into today’s business workflows, providing frameworks to decide when and how to use it effectively. You’ll gain hands-on experience designing Generative AI tools to create business value and programming basic LLM-driven applications in Python. Finally, the course will ask you to become conversant with the big questions about Generative AI, to debate the moral, philosophical, and ethical challenges inherent in these systems and technologies.

This course covers the design and development of Generative AI-enhanced business applications.  We will discuss a framework for integrating Gen AI tools and capabilities into business processes, tasks and workflows.  Students will learn how to program with Gen AI tools and LLMs using Python and APIs, including methods for dynamic task automation and workflow coordination using agentic design. The course also covers the use of Retrieval-Augmented Generation (RAG) to access private knowledge bases built on organizational data. By the end of the course, students will be equipped with the technical and conceptual skills to design and build generative AI applications tailored to modern business needs.
 

MGC 461 is based on classical principles of argument and persuasion and current communication research that reveals the keys to having influence in a global business world. Its goal is to develop professional-level presentation skills, individually and in teams. The course is performance based; all class sessions require students to speak and interact extensively. Over two mini-semesters, students develop professional presentation skills through four individual and two team-based speaking assignments, for which they receive extensive, individualized feedback. They also learn a systematic process for assessing their own communication, and for giving feedback to others. At the end of the course students will have developed skills key to success in any field: the ability to adapt effectively to a given audience, and to express ideas with an appropriate combination of logic and feeling.

This course is fundamentally about how to learn from data. We will demonstrate the stark differences in the goals and implementations of descriptive, predictive, and causal analysis. Students will be expanding their analytic toolkit in each of these areas and learning how to select the right tool for the job. Particular attention will be paid to the usage of these analyses to drive business decisions, and how to respond to data analytics questions in job interviews.

This course presents the basics of financial accounting and will provide a framework for analyzing financial data. The course begins with an overview of the four financial statements and the accounting underlying those statements, and includes ratio analysis based on those statements. The course will then survey topics in corporate finance, centered on the analysis of financial data. The course includes a survey of financial metrics used to analyze operations, then proceeds to a discussion of project evaluation with a focus on relevant cash flows, and then investigates the appropriate required rate of return to be used in evaluating those cash flows. The course also covers methods for estimating firm value and includes an overview of options and futures.

The purpose of MKT414 is to help future managers make good decisions by preparing them to analyze the environment in which their firm operates and to arrive at an appropriate pricing policy for their product or service. More specifically, the objectives of the course are: 1) to develop an understanding of the relationship between a firm’s environment (e.g., cost, demand, competition, and legal aspects) and its optimal pricing strategy, and 2) to develop skills in applying this understanding. There are several components to the course: elasticity of demand and relevant costs, price discrimination and market segmentation, and competitive pricing. Students will learn the fundamentals of economic-value analysis and break-even analysis and will be made familiar with strategies such as bundling, tie-in sales, quantity discounts, product-line pricing, and demand buildup. The course will cover ways of predicting competitor-pricing responses, and it will discuss a firm’s legal environment as it pertains to pricing.

Operations Management introduces the concepts and skills needed to design, manage, and improve service and manufacturing operations. The course develops a managerial perspective of the operations function and an appreciation of the role that operations plays in creating and maintaining a firm’s competitive edge. The course introduces process analysis, performance measurement systems for operations, and production control systems.

Quantitative models and case studies apply these skills to service process management, manufacturing, inventory control, supply chain management, and project management. The course highlights the role of effective operations management in the strategic direction of the firm as well as the connections between operations and other functional areas.