Python and Applied AI

Foundations in Python and applied AI for business applications

Python and Applied AI is designed for professionals with no prior programming experience who want to build hands-on AI capability from the ground up. This pathway equips business professionals with the skills to become AI Business Technology Professionals, combining foundational Python programming, core AI literacy, and strategic frameworks for generative AI. Students gain practical experience applying Python to design and prototype AI-enabled applications for modern business needs.

Students will complete three 2.5-credit courses.

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GBA 465: Introduction to Python Programming

Created in 1989, Python is one of the most popular programming languages in the world, known for its power, simplicity, and flexibility. Data analysts—including business and marketing analysts, data scientists, management consultants, and product managers—use Python alongside powerful third-party packages such as NumPy, Pandas, Matplotlib, and Seaborn to import, explore, manipulate, analyze, and visualize data. By completing this course, you will learn to think like a programmer by breaking down complex problems into smaller steps and pseudocode, write basic computer programs in Python to implement your ideas, and perform foundational data analytics to support informed decision-making.

GBA 478: AI and Business

GBA478 covers the application of generative AI technologies across diverse business contexts, helping you understand how to integrate these tools into modern workflows. The course provides frameworks for deciding when and how to use generative AI effectively, along with hands-on experience designing AI tools that create business value and programming basic LLM-driven applications in Python. It also encourages you to engage with broader questions about generative AI, including the moral, philosophical, and ethical challenges inherent in these technologies.

GBA 479: Generative AI in Business Applications

This course focuses on the design and development of generative AI–enhanced business applications, with a framework for integrating Gen AI tools and capabilities into business processes, tasks, and workflows. Students learn to program with Gen AI tools and large language models using Python and APIs, build AI-driven systems that execute dynamic tasks, and develop multi-agent architectures for complex workflows. The course also explores Retrieval-Augmented Generation (RAG) to access private knowledge bases built on organizational data. By the end of the course, students will have the technical and conceptual skills to design and build generative AI applications tailored to modern business needs.