AI Coding, Analytics, and Synthetic Data

This unit introduces practical coding tools, core data-analytic workflows, and synthetic data techniques used to support AI-driven business decisions. It builds on foundational concepts from earlier units and prepares students to design and evaluate AI prototypes and data strategies in the final unit.

Learning Objectives

• Analyze business problems and datasets to select appropriate coding tools, libraries, and development environments for AI prototyping
• Apply programming and low-code tools to ingest, clean, transform, and document business data for analysis and model input
• Demonstrate core data analytics techniques (descriptive statistics, visualization, and feature engineering) to generate actionable business insights
• Evaluate synthetic data generation methods for utility, privacy preservation, and bias mitigation, and validate synthetic datasets against real-data benchmarks