Key Takeaways:
- The University of Virginia’s Master of Science in Business Analytics (MSBA) program offers a holistic approach to AI learning, focusing on technical mastery and leadership training.
- The program’s AI learning strategy is built on five pillars: developing skills in the latest AI and machine learning systems, teaching AI and analytics as an end-to-end system, emphasizing real-time, real-organization AI challenges, preparing students to lead AI initiatives, and integrating AI into a robust organizational strategy.
- The MSBA program is designed to deliver business leaders who can understand and evaluate AI models, transform data infrastructure to support AI, manage cross-functional AI projects, implement AI ethically and at scale, and demonstrate business impact from AI investments.
- The program’s curriculum is designed to evolve with the rapidly changing field of AI, with a focus on expanding generative AI and LLM-based coursework, prompt engineering, AI-assisted analytics, and multimodal modeling.
Introduction to AI Learning
In December 2025, The New York Times reported that artificial intelligence (AI) is "the hot new college major," with students flocking to degree programs that focus on developing AI-related skills. However, for such a new and rapidly evolving field, "AI learning" is only loosely defined, with no set standard for which technical skills, real-world applications, and theoretical concepts like AI ethics should be taught. According to Professor Jingjing Li, co-academic director of the UVA MSBA, "It is not enough to teach how to build models. Organizations need business leaders who understand how AI reshapes culture, operations, customer experience and strategy."
The UVA MSBA Program
The University of Virginia’s Master of Science in Business Analytics (MSBA) program, presented jointly by the McIntire School of Commerce and Darden School of Business, has been embedding a holistic approach to AI learning throughout its 12-month degree program for working professionals since it launched in 2018. The program’s AI learning strategy is built on five pillars: developing skills in the latest AI and machine learning systems, teaching AI and analytics as an end-to-end system, emphasizing real-time, real-organization AI challenges, preparing students to lead AI initiatives, and integrating AI into a robust organizational strategy. As Professor Raj Venkatesan, co-academic director of the program, noted, "The MSBA is intentionally designed as a fully integrated analytics and AI curriculum in which every module builds toward a real organizational application."
Technical Mastery and Leadership Training
To deliver on the needs of organizations, the UVA MSBA program uses a business lens to focus AI skill development across two areas: technical mastery and leadership training. Students gain technical mastery by developing critical skills in SQL, Spark, large language models (LLMs), survival analysis, deep learning, time series analyses, and more, so they can build and assess AI models responsibly. Leadership training is achieved through the program’s capstone projects, strategy and ethics courses, global immersions, and business sponsor engagement, all of which help students assess how AI can create business value, implement AI responsibly and ethically, and manage AI projects and teams.
Real-World Applications
The UVA MSBA program is divided into five modules, each concluding with a team capstone project. Each module includes a set of coordinated courses, and across modules, students develop skills in analytics, data engineering, strategy, communication, and project design. Capstones aren’t hypothetical exercises; rather, they are sponsored by real organizations and require students to build AI-powered applications that address the real needs of sponsor organizations. For example, in Module 2, students develop a machine learning model to predict donors likely to participate in an upcoming fundraising campaign for a large university and deliver AI-informed recommendations. As Professor Li noted, "Students solve progressively more sophisticated AI problems. They have the opportunity to actually build production-style AI applications, such as web and mobile apps powered by machine learning and deep-learning models."
From the Classroom to the Workplace
Most UVA MSBA students pursue their degree while continuing to work full-time, and as such, students often take AI learning from courses and projects back to their workplace immediately. For example, one student working in healthcare built an NLP model for patient feedback inspired by the Module 4 FDA adverse drug reaction project, delivering major time savings for the student’s clinical operations team. Another student who worked for a federal government agency used survival analysis methods taught in Module 3 to build attrition models that informed the agency’s workforce planning policy. As Professor Venkatesan noted, "Our capstone experiences make these stories commonplace among graduates."
Evolving with AI
Though the MSBA is less than 10 years old, AI has evolved and advanced dramatically during that time. The MSBA curriculum is designed for the next phase of AI evolution, with a focus on expanding generative AI and LLM-based coursework, prompt engineering, AI-assisted analytics, and multimodal modeling. As Professor Li noted, "We built AI into the foundation of the curriculum long before the generative AI boom. Students choose the UVA MSBA because it provides the AI skills the market now demands—and it has been doing so for years." The program will continue to expand partner-sponsored projects and global immersion experiences to deliver that learning, ensuring that graduates are equipped to drive AI-enabled innovation inside complex organizations.