At a Glance

Name: Synthetic Dataset Generation with LLM Agent and Statistics
Price: Free CAD
Rating: 5.0 (1 reviews)
Author: tenzin_migmar, jianping_ye

Ever wonder how companies train ML models without exposing sensitive data? Synthetic data is the answer. In this project, you'll use LangChain, OpenAI's GPT-5 mini, and statistical methods to build a pipeline that generates realistic, privacy-safe datasets from scratch. You'll learn when to use LLMs versus statistical sampling, how to validate quality, and how to check for privacy leaks. Walk away ready to create synthetic data for any domain.

With data privacy regulations tightening and real-world datasets often locked behind compliance walls, synthetic data generation has become a must-have skill for ML practitioners. This guided project shows you how to build a complete synthetic data pipeline using LangChain, GPT-5 mini, and proven statistical techniques. You won't just generate random noise, you'll create datasets that preserve the statistical properties and correlations of real data while containing zero actual sensitive information. From healthcare records to e-commerce transactions, you'll learn to synthesize data that's useful, realistic, and safe to share.

What You'll Learn

By the end of this project, you will be able to:

Build hybrid synthetic data generators with LLMs and statistical methods: Understand when to use GPT-5 for text and structured data versus statistical sampling for numerical fields—and how to combine both for best results.
Implement correlation-preserving generation with copula methods: Use the Synthetic Data Vault (SDV) library to create multi-variate data that maintains realistic relationships between features, not just independent random values.
Validate synthetic data quality and privacy: Apply statistical tests, visualizations, and privacy checks to ensure your synthetic datasets are both useful and safe from re-identification risks.

Who Should Enroll

Data scientists and ML engineers who need training data but face privacy, compliance, or data scarcity constraints—and want a practical solution they can implement immediately.
Software developers working on testing and QA who need realistic, diverse datasets without the hassle of anonymizing production data or waiting on data access requests.
Privacy and compliance professionals looking to understand synthetic data from a technical perspective so they can evaluate solutions and guide their teams effectively.

Why Enroll

Synthetic data isn't just a workaround, it's becoming the standard for responsible AI development. This project gives you hands-on experience with the techniques that power production-grade synthetic data systems: LLM-based generation, Gaussian copulas, conditional sampling, and privacy validation. You'll finish with a working pipeline you can adapt to your own domain, plus the understanding to know when synthetic data is the right choice and when it isn't.

What You'll Need

You should be comfortable with Python and have basic familiarity with pandas and NumPy. Some exposure to machine learning concepts is helpful but not required. All dependencies are pre-configured in the environment, and the project runs best on current versions of Chrome, Edge, Firefox, or Safari.

Offered By: IBMSkillsNetwork

Synthetic Dataset Generation with LLM Agent and Statistics

At a Glance

What You'll Learn

Who Should Enroll

Why Enroll

What You'll Need