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.

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 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.

This course introduces the basics of developing web-based AI and business analytics applications, with three objectives. The primary objective is to prepare students for projects that require not only the analysis of data or the use of modern AI techniques, but also the creation of an efficient and fully functional user interfaces supporting many concurrent users. The second objective is to pave the way for subsequent courses so that students can use what is taught in this course to create a front-end user interface for what they learn in other analytics courses. The third objective is to help students develop a personal branding site showcasing their knowledge base and skill sets.

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 class offers an introduction to big data concepts, environments, processes, and tools from the perspective of data analysts and data scientists. The course sets the scene for the emergence of big data as an important trend in the business world and explain the technical architectures that make analyzing data at scale possible. The hands-on portion of the class focuses on the major tools of the Hadoop big data ecosystem such as HDFS, Hive, Zeppelin, Spark and Spark MLlib. In addition, students gain a broad understanding of the role of MapReduce, Tez, Impala, YARN, and other big data technologies. Students use a live Hadoop cluster hosted on Amazon Web Services (AWS) and thus also have an opportunity to understand the characteristics of cloud computing and storage solutions and their growing role in big data analytics.

This course aims to train practitioners of analytics methods to construct, evaluate, and apply machine learning (ML) models in a variety of business applications using modern tools. The course covers programming tools for handling data, computational frameworks, cloud platforms, and an array of advanced ML algorithms. The course emphasizes hands-on work through class exercises, homework assignments, and projects. The course is self-contained, but basic programming skills are required.

The rise of social media has empowered customers in an unprecedented way. They are well connected with each other through platforms like Facebook and Twitter, and they can easily express and distribute their comments, criticisms, or endorsements publicly to large audiences in real time. This fundamental media revolution not only forces companies to actively manage their presence and engage with customers on social media platforms but also offers them a golden opportunity to extract intelligence from the vast amount of unstructured data. Technology and strategies are increasingly intertwined in this new frontier of innovation and competition. This course draws on a unique blend of social media strategies and the rapidly evolving information technologies supporting these strategies. We will discuss issues related to the monitoring and analyzing of social media for companies in different industries. The learning objectives of this course include: (1) gaining a deeper understanding of social media and its implications in the business world; (2) becoming comfortable with text data; and (3) being able to understand and apply several commonly used methods to analyze text data.
MS Prerequisites: GBA 462, (GBA464 or GBA485A)

This course focuses on database design, management, and warehousing concepts to support AI and analytics efforts. The course introduces ER diagrams, SQL programming and related frameworks for data retrieval and transformation as well some advanced SQL features such as user-defined functions, triggers and procedures.

GBA468 expands and develops the students’ analytical toolkit to include prescriptive analytics methodologies for managerial decision-making. The coursework follows the FACt approach to business problem solving and will cover diverse applications in operational management, supply chain analytics, marketing, and finance. Modeling techniques covered include: Decision tree models Constrained optimization models Monte Carlo simulation The course will be taught primarily using Python.