IS7065: Generative AI for Business is a graduate course designed to equip students with the knowledge and skills needed to leverage generative AI (GenAI) and Large Language Models (LLMs) in business contexts. The course is offered in various formats, including online, in-person, and hybrid sections, to accommodate diverse learning preferences and schedules.
This course will delve into the core mechanisms of GenAI, focusing on Large Language Models (LLMs). Participants will explore advanced techniques in prompt engineering, retrieval augmented generation (RAG), fine-tuning LLMs, and ensuring the safety and ethical use of GenAI. The curriculum balances theoretical knowledge with hands-on exercises, enabling participants to apply GenAI innovations to real-world business challenges effectively. By demystifying the complexities of LLMs, this course aims to empower students to leverage these powerful models for creating innovative solutions and navigating the dynamic landscape of generative AI with confidence. Upon completion of the course, students should be able to:
Articulate the essentials of LLM architecture, training methodologies, and business applications.
Implement effective prompt engineering strategies to develop robust business applications using LLMs.
Integrate RAG into LLM applications to provide coherent and contextually relevant responses.
Customize LLMs using fine-tuning, balancing model capabilities and computational demands.
Address safety and ethics issues in LLM applications.
Engage in thoughtful discussions on the transformative potential of GenAI, recognizing both the opportunities and challenges it introduces.
IS6030: Data Management is a foundational graduate course designed to equip students with essential skills and knowledge for effective data management in organizational settings. Emphasizing database use and design, this course covers the practical application of Structured Query Language (SQL) for efficient data storage, manipulation, and querying. The course is available in multiple formats, including online, in-person, and hybrid sections, to meet a range of learning preferences and schedules.
Upon successful completion of this course, students will be able to:
Articulate the importance of data management in business environments.
Explain core concepts of effective database design.
Use SQL for various data management activities, including data definition, manipulation, and querying.
Identify and define data requirements for specific projects and extract relevant data from existing databases.
This course emphasizes the role of Information Systems (IS) and Information Technology (IT) as critical resources in managerial decision-making. With over $1 trillion spent annually on technology and IS, understanding and leveraging digital tools is essential for modern business operations. This course prepares undergraduate students to understand digital technologies, recognize the business opportunities they create, and apply productivity tools to enhance business strategies.
Students will gain foundational technology literacy, develop skills to discuss key IS concepts, explore current IT capabilities and trends, and master tools for data-informed decision-making. Upon completion of this course, students will be able to:
Understand the role of IS in driving business strategy, competitive edge, and decisions.
Leverage data, BI, and AI to transform information into actionable insights.
Explore how networks, telecommunications, and mobile tech support business connectivity.
Recognize ethical, privacy, and security issues in digital data and IS.
Examine the impact of Web 2.0, social networks, e-commerce, and AI on business models.
Understand how businesses acquire and implement IS to enhance functions and engage customers.
This course introduces students in the Master of Health Informatics program to foundational principles of data modeling and database design, with an emphasis on applications in health informatics. Through hands-on practice, students will build skills in designing databases, creating data models, and working directly with health data.
Students will gain proficiency in data structuring, model mapping, and SQL-based database implementation, preparing them to design and manage databases effectively. Upon completion of this course, students will:
Design relational databases to address specific health-related problems.
Develop data models that align with healthcare organizations' needs and objectives.
Translate conceptual data models into logical and physical models tailored for health informatics.
Build relational databases and construct SQL queries for data retrieval and reporting in healthcare settings.
Implement database integrity constraints to maintain data accuracy and reliability in health data.
Apply data management skills to generate reports that support decision-making in health informatics.