15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
- Data protection officers
- Information security professionals
- Data architects
- Enterprise architects
- Analytics professionals
- Risk and compliance officers
- IT managers
- AI governance professionals
- Data governance specialists
- Software developers and testers
- Research and innovation professionals
- Professionals responsible for enterprise data management and privacy-preserving technologies
Session Objectives
- Understand the fundamentals of synthetic data generation.
- Master generative adversarial networks (GANs) for creating synthetic data.
- Utilize variational autoencoders (VAEs) for latent space modeling and data generation.
- Implement simulation-based methods for generating domain-specific synthetic data.
- Design and build synthetic data pipelines for model training and testing.
- Optimize synthetic data generation for data fidelity and diversity.
- Troubleshoot and address common challenges in synthetic data generation.
- Implement techniques for evaluating the quality of synthetic data.
- Integrate synthetic data with real-world machine learning workflows.
- Understand how to handle privacy considerations in synthetic data generation.
- Explore advanced synthetic data generation techniques (e.g., diffusion models, transformer-based generators).
- Apply real world use cases for synthetic data in various domains.
- Leverage synthetic data generation libraries and tools for efficient development.
About the Course
This advanced training equips professionals with the knowledge and practical skills to generate, evaluate, govern, and deploy synthetic data while strengthening privacy protection across enterprise data environments. Participants will explore how synthetic data can support analytics, artificial intelligence, software development, testing, research, and data sharing while reducing exposure to sensitive real-world information.The course combines synthetic data generation techniques with modern privacy engineering principles. Participants will learn how to assess privacy risks, select appropriate generation methods, evaluate synthetic data quality and utility, implement privacy-preserving techniques, and establish governance frameworks for responsible enterprise use.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of synthetic data generation.
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Workshop 1.2:
Overview of GANs, VAEs, and simulation-based methods.
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Workshop 1.3:
Setting up a synthetic data generation development environment.
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Workshop 1.4:
Introduction to synthetic data generation libraries and tools.
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Workshop 1.5:
Best practices for synthetic data generation.
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Workshop 2.1:
Implementing GANs for creating synthetic data.
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Workshop 2.2:
Utilizing different GAN architectures (DCGAN, StyleGAN).
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Workshop 2.3:
Designing and building GAN models for specific data types.
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Workshop 2.4:
Optimizing GAN training and evaluation for synthetic data quality.
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Workshop 2.5:
Best practices for GANs.
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Workshop 3.1:
Implementing VAEs for latent space modeling and data generation.
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Workshop 3.2:
Utilizing VAEs for creating diverse and realistic synthetic data.
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Workshop 3.3:
Designing and building VAE models for data synthesis.
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Workshop 3.4:
Optimizing VAEs for latent space representation and data fidelity.
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Workshop 3.5:
Best practices for VAEs.
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Workshop 4.1:
Implementing simulation-based methods for generating domain-specific synthetic data.
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Workshop 4.2:
Utilizing physical simulations and procedural generation.
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Workshop 4.3:
Designing and building simulation environments.
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Workshop 4.4:
Optimizing simulations for data realism and variability.
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Workshop 4.5:
Best practices for simulation-based methods.
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Workshop 5.1:
Designing and building synthetic data pipelines for model training and testing.
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Workshop 5.2:
Implementing data augmentation and transformation techniques.
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Workshop 5.3:
Utilizing data synthesis for specific machine learning tasks.
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Workshop 5.4:
Optimizing pipelines for data generation efficiency.
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Workshop 5.5:
Best practices for synthetic data pipelines.
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Workshop 6.1:
Optimizing synthetic data generation for data fidelity and diversity.
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Workshop 6.2:
Utilizing evaluation metrics and data quality assessment.
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Workshop 6.3:
Implementing techniques for improving data realism and variability.
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Workshop 6.4:
Designing scalable synthetic data generation strategies.
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Workshop 6.5:
Best practices for data fidelity and diversity.
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Workshop 7.1:
Debugging common challenges in synthetic data generation.
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Workshop 7.2:
Analyzing data quality and model performance.
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Workshop 7.3:
Utilizing troubleshooting techniques for problem resolution.
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Workshop 7.4:
Resolving common synthetic data issues.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing techniques for evaluating the quality of synthetic data.
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Workshop 8.2:
Utilizing statistical tests and machine learning metrics.
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Workshop 8.3:
Designing and building evaluation frameworks.
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Workshop 8.4:
Optimizing evaluation for data utility and realism.
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Workshop 8.5:
Best practices for data quality evaluation.
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Workshop 9.1:
Integrating synthetic data with real-world machine learning workflows.
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Workshop 9.2:
Utilizing APIs and data connectors.
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Workshop 9.3:
Implementing synthetic data in model training and validation.
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Workshop 9.4:
Optimizing integration for specific machine learning tasks.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Understanding how to handle privacy considerations in synthetic data generation.
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Workshop 10.2:
Utilizing differential privacy and other privacy-preserving techniques.
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Workshop 10.3:
Designing and building privacy-preserving synthetic data pipelines.
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Workshop 10.4:
Optimizing data generation for privacy compliance.
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Workshop 10.5:
Best practices for privacy.
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Workshop 11.1:
Exploring advanced synthetic data generation techniques (diffusion models, transformer-based generators).
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Workshop 11.2:
Utilizing diffusion models for high-quality data synthesis.
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Workshop 11.3:
Implementing transformer-based generators for sequential data.
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Workshop 11.4:
Designing and building advanced synthetic data solutions.
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Workshop 11.5:
Optimizing advanced techniques for specific applications.
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Workshop 11.6:
Best practices for advanced techniques.
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Workshop 12.1:
Implementing synthetic data for medical imaging and healthcare.
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Workshop 12.2:
Utilizing synthetic data for autonomous vehicle training.
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Workshop 12.3:
Implementing synthetic data for financial fraud detection.
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Workshop 12.4:
Utilizing synthetic data for natural language processing.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing TensorFlow, PyTorch, and specialized synthetic data libraries.
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Workshop 13.2:
Implementing synthetic data models with libraries.
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Workshop 13.3:
Designing and building synthetic data pipelines.
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Workshop 13.4:
Optimizing library usage for efficient development.
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Workshop 13.5:
Best practices for library implementation.
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Workshop 14.1:
Implementing data evaluation and monitoring for synthetic data.
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Workshop 14.2:
Utilizing metrics for data fidelity and model performance.
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Workshop 14.3:
Designing and building monitoring dashboards.
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Workshop 14.4:
Optimizing monitoring for real-time insights.
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Workshop 14.5:
Best practices for monitoring.
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Workshop 15.1:
Emerging trends in synthetic data generation.
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Workshop 15.2:
Utilizing AI for automated synthetic data generation.
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Workshop 15.3:
Implementing synthetic data in edge computing environments.
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Workshop 15.4:
Best practices for future applications.
