{"product_id":"r-for-synthetic-data-generation-data-simulation-privacy-protection-and-machine-learning-testing-in-r-9798254192244","title":"R for Synthetic Data Generation: Data Simulation, Privacy Protection, and Machine Learning Testing in R","description":"\u003cp\u003e • Author(s): Brooks Saint\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Probability \u0026amp; Statistics - General\u003c\/p\u003e\u003cp\u003e\u003cb\u003eR FOR SYNTHETIC DATA GENERATION: DATA SIMULATION, PRIVACY PROTECTION, AND MACHINE LEARNING TESTING IN R\u003c\/b\u003e\u003cbr\u003eNo 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.\u003cbr\u003eThis 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.\u003cbr\u003eInstead of vague concepts and academic explanations, this guide walks through practical workflows used in analytics, machine learning, and production systems.\u003cbr\u003eInside, you'll learn how to: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eGenerate realistic synthetic datasets that preserve statistical structure and relationships\u003c\/li\u003e\n\u003cli\u003eSimulate complex systems, including tabular, hierarchical, and time-series data\u003c\/li\u003e\n\u003cli\u003eApply generative methods such as copulas, Bayesian networks, and deep learning approaches\u003c\/li\u003e\n\u003cli\u003eBalance privacy and utility using practical techniques, including differential privacy concepts\u003c\/li\u003e\n\u003cli\u003eDetect and eliminate bias introduced during synthetic data generation\u003c\/li\u003e\n\u003cli\u003eValidate synthetic data using statistical tests and real-world model performance\u003c\/li\u003e\n\u003cli\u003eUse synthetic data to improve machine learning models and handle data scarcity\u003c\/li\u003e\n\u003cli\u003eBuild scalable pipelines for testing, QA, and production systems\u003c\/li\u003e\n\u003c\/ul\u003eThis book is for: \u003cul\u003e\n\u003cli\u003eData scientists who need reliable training data without privacy risks\u003c\/li\u003e\n\u003cli\u003eAnalysts and engineers building test datasets for real systems\u003c\/li\u003e\n\u003cli\u003eOrganizations working with sensitive data that cannot be shared\u003c\/li\u003e\n\u003cli\u003eR users looking to apply synthetic data techniques in real workflows\u003c\/li\u003e\n\u003c\/ul\u003eWhat makes this different: \u003cbr\u003eMost 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.\u003cbr\u003eIf 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.\u003cbr\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47775367004311,"sku":"9798254192244","price":1727.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798254192244.webp?v=1777989914","url":"https:\/\/atlanticbooks.com\/products\/r-for-synthetic-data-generation-data-simulation-privacy-protection-and-machine-learning-testing-in-r-9798254192244","provider":"Atlantic Books","version":"1.0","type":"link"}