Generative AI for Synthetic Data Modelling with Python SDV

Generating Synthetic Data with GenAI tools and Python: Techniques, Model Selection, and Real-World Applications

Created by: Lucas Whitaker
A new instructor

What Will I Learn?

• Master Python techniques for synthetic data generation with SDV.
• Understand the importance and applications of synthetic data.
• Generate high-quality synthetic data using GANs and VAEs.
• Preprocess real-world data for effective synthetic data modeling.
• Select and implement the best models for synthetic data generation.
• Evaluate synthetic data quality with SDMetrics.
• Ensure data privacy and integrity in synthetic data generation.
• Apply synthetic data techniques to healthcare, finance, and retail.
• Handle complex datasets with advanced synthetic data techniques.
• Explore future trends and technologies in synthetic data generation.

Requirements

• Basic Programming Knowledge: Familiarity with Python programming is recommended.
• Fundamental Data Science Concepts: A basic understanding of data science principles is recommended.
• oftware Requirements: Learners should have access to a computer with an internet connection and the ability to install necessary software packages such as Python, NumPy, Pandas, and SDV.
• Interest in Data Analysis: Enthusiasm for data analysis and a willingness to learn about synthetic data generation and its applications across various industries.
• No advanced programming experience or deep knowledge of machine learning is required. This course is designed to guide you through all necessary concepts and tools from the ground up, making it accessible to beginners eager to explore synthetic data generation.

Target audience

• Professionals looking to enhance their skills in data generation, model training, and data augmentation.
• Individuals working with machine learning models who need high-quality synthetic data for training, testing, and validating their algorithms.
• Scholars conducting research in fields such as healthcare, finance, and social sciences who require synthetic data to ensure privacy and compliance with ethical standards.
• Developers interested in incorporating synthetic data generation into their applications, particularly those working on projects that involve data privacy, data sharing, and compliance with regulatory requirements.
• Business analysts and decision-makers seeking to understand the potential of synthetic data in driving business insights, improving decision-making processes, and maintaining data privacy.
• Learners and enthusiasts with a basic understanding of programming and data science who are curious about synthetic data generation and its real-world applications. This course offers an entry point to explore this growing field.