Applied AI for Advanced Analytics

Foundations of AI · Agentic AI · Enterprise Adoption

Applied AI for Advanced Analytics is a hands-on course in Wake Forest’s MSBA program that prepares students to use modern AI in analytical work. The course is organized into three sections: Foundations of AI, Agentic AI, and Enterprise Adoption—moving from understanding how AI works, to building agent-driven workflows, to applying AI within organizations.

Foundations of AI

Understand how modern AI learns and how to evaluate its results. Students explore machine learning, neural networks, transformers, and large language models, then train a neural network and a tiny transformer from scratch. Using local models, students learn to train, compare, and evaluate AI models for business applications. Students will fine-tune models for business applications, practice prompt engineering, and design tools that enable AI to work with data and software.

Artificial neural network with input, hidden, and output layers of connected neurons.

An AI assistant connected to coding, documents, tools, and a task checklist.

Agentic AI

Manage teams of AI agents that connect to data, software, and analytical tools. Students use Claude Code and Codex alongside Git and Git worktrees to divide projects into parallel tasks, provide context, track changes, and review and combine results. The goal is to learn how to efficiently manage a team of AI agents working in parallel on projects, with clear responsibilities and reliable outcomes.

Enterprise Adoption

Integrate AI with private organizational data to build reliable analytical workflows. Students learn to prevent AI work slop by validating outputs, ensuring quality and reproducibility, and protecting data privacy. The section emphasizes best practices for integration at scale, including secure data access, repeatable processes, and ongoing quality checks as AI moves into enterprise use.

Rows of server racks in a modern corporate data center.

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