Coding Tools and Synthetic Data

This unit introduces the development toolchain and data practices required for creating and evaluating generative AI systems. Students will work with coding environments and data-analytics workflows, learn methods for producing and validating synthetic datasets, and examine privacy, bias, and documentation practices that support responsible model development and deployment.

Learning Objectives

• Analyze features, workflows, and trade-offs of modern coding tools and environments (IDE, notebooks, version control, CI/CD) to select appropriate toolchains for generative AI projects
• Apply data-cleaning, exploratory data analysis, and visualization techniques to transform raw data into well-documented datasets suitable for model training and evaluation
• Demonstrate methods for creating, augmenting, and validating synthetic datasets (including sampling strategies and utility/fidelity metrics) to supplement or replace sensitive real-world data
• Evaluate ethical, privacy, and bias-related risks associated with synthetic data and development pipelines, and propose mitigation, documentation, and governance strategies for responsible AI practice