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"item_title" : "R for Synthetic Data Generation",
"item_author" : [" Brooks Saint "],
"item_description" : "R FOR SYNTHETIC DATA GENERATION: DATA SIMULATION, PRIVACY PROTECTION, AND MACHINE LEARNING TESTING IN RNo real data? No problem. With the right approach, you can build realistic, high-quality datasets that power models, protect privacy, and stress-test systems without risk.This book is built for practitioners who need more than theory. It shows how to generate, validate, and deploy synthetic data using R in real-world environments where accuracy, privacy, and reliability actually matter.Instead of vague concepts and academic explanations, this guide walks through practical workflows used in analytics, machine learning, and production systems.Inside, you'll learn how to: Generate realistic synthetic datasets that preserve statistical structure and relationshipsSimulate complex systems, including tabular, hierarchical, and time-series dataApply generative methods such as copulas, Bayesian networks, and deep learning approachesBalance privacy and utility using practical techniques, including differential privacy conceptsDetect and eliminate bias introduced during synthetic data generationValidate synthetic data using statistical tests and real-world model performanceUse synthetic data to improve machine learning models and handle data scarcityBuild scalable pipelines for testing, QA, and production systemsThis book is for: Data scientists who need reliable training data without privacy risksAnalysts and engineers building test datasets for real systemsOrganizations working with sensitive data that cannot be sharedR users looking to apply synthetic data techniques in real workflowsWhat makes this different: Most books explain synthetic data at a surface level. This one focuses on execution how to build systems that work, how to test them, and how to avoid the subtle failures that make synthetic data useless.If you want to move from theory to real implementation and actually trust the data you generate, this book gives you the tools to do it.",
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R for Synthetic Data Generation : Data Simulation, Privacy Protection, and Machine Learning Testing in R
by Brooks Saint
Overview
R FOR SYNTHETIC DATA GENERATION: DATA SIMULATION, PRIVACY PROTECTION, AND MACHINE LEARNING TESTING IN R
No real data? No problem. With the right approach, you can build realistic, high-quality datasets that power models, protect privacy, and stress-test systems without risk.
This book is built for practitioners who need more than theory. It shows how to generate, validate, and deploy synthetic data using R in real-world environments where accuracy, privacy, and reliability actually matter.
Instead of vague concepts and academic explanations, this guide walks through practical workflows used in analytics, machine learning, and production systems.
Inside, you'll learn how to:
- Generate realistic synthetic datasets that preserve statistical structure and relationships
- Simulate complex systems, including tabular, hierarchical, and time-series data
- Apply generative methods such as copulas, Bayesian networks, and deep learning approaches
- Balance privacy and utility using practical techniques, including differential privacy concepts
- Detect and eliminate bias introduced during synthetic data generation
- Validate synthetic data using statistical tests and real-world model performance
- Use synthetic data to improve machine learning models and handle data scarcity
- Build scalable pipelines for testing, QA, and production systems
- Data scientists who need reliable training data without privacy risks
- Analysts and engineers building test datasets for real systems
- Organizations working with sensitive data that cannot be shared
- R users looking to apply synthetic data techniques in real workflows
Most books explain synthetic data at a surface level. This one focuses on execution how to build systems that work, how to test them, and how to avoid the subtle failures that make synthetic data useless.
If you want to move from theory to real implementation and actually trust the data you generate, this book gives you the tools to do it.
This item is Non-Returnable
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Details
- ISBN-13: 9798254192244
- ISBN-10: 9798254192244
- Publisher: Independently Published
- Publish Date: March 2026
- Dimensions: 9 x 6 x 0.27 inches
- Shipping Weight: 0.4 pounds
- Page Count: 128
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