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Practical Synthetic Data Generation /
One challenge with big data and other secondary analytics initiatives is getting access to large and diverse data. Secondary analytics allow insights beyond the questions that data initially collected can answer. This practical book introduces techniques for generating synthetic data-fake data gener...
| Autores principales: | , , |
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| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
O'Reilly Media, Inc.,
2020.
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| Edición: | 1st edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | One challenge with big data and other secondary analytics initiatives is getting access to large and diverse data. Secondary analytics allow insights beyond the questions that data initially collected can answer. This practical book introduces techniques for generating synthetic data-fake data generated from real data-that can provide secondary analytics to help you understand customer behaviors, develop new products, or generate new revenue. CTOs, CIOs, and directors of analytics will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps of synthetic data generation from real data sets. Business leaders will examine how synthetic data can help accelerate time to a solution. |
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| Descripción Física: | 1 online resource (175 pages) |