0 or 2 credits
Fall 2026 Laboratory Lecture Lower Division
This course introduces students to modern data-driven decision making with an emphasis on the essential roles of business analytics and artificial intelligence (AI). Students examine enterprise data and analytics infrastructure, analytics approaches from descriptive to generative, and the use of AI to support analysis and decision making. Through hands-on exercises using R, Python, and real-world business cases, students develop foundational AI and analytics literacy, interpretation skills, and responsible judgment for AI-enabled business contexts.
Learning Outcomes1Explain the roles of analytics and AI in modern data-driven decision making within organizations.
2Describe the four phases of the modern data-driven decision-making process and how they interact with AI.
3Differentiate the major analytics approaches, including descriptive, diagnostic, predictive, prescriptive, and AI enabled generative analytics.
4Apply AI-assisted tools to support data analysis, interpretation, and communication of insights.
5Evaluate analytics and AI-generated outputs for reliability, limitations, and decision relevance.
6Communicate insights clearly and responsibly to support informed business decisions.
Restrictions
made by Purdue students.