Synthetic Data Generation & Use in AI

• Chapter 1.1: What is Synthetic Data?

• Chapter 1.2: Types of Synthetic Data (Tabular, Image, Text, Time-Series)

• Chapter 1.3: Benefits Over Real Data – Privacy, Cost, Scalability

• Chapter 1.4: When (and When Not) to Use Synthetic Data in AI

• Chapter 2.1: Overview of Synthetic Data Generators (Gretel, MOSTLY AI, SDV)

• Chapter 2.2: Using GANs, VAEs, and LLMs for Synthetic Data

• Chapter 2.3: Prompt-Based Data Synthesis for NLP Tasks

• Chapter 2.4: Preprocessing Real Data for Synthetic Modeling

• Chapter 3.1: GAN-based Generation for Images and Video

• Chapter 3.2: Synthetic Tabular Data with Statistical Models

• Chapter 3.3: Balancing and Augmenting Datasets with Synthetic Samples

• Chapter 3.4: Using LLMs to Generate Domain-Specific Text Data

• Chapter 4.1: Utility Metrics – How “Useful” is Synthetic Data?

• Chapter 4.2: Privacy Metrics – Differential Privacy, k-Anonymity, Membership Inference

• Chapter 4.3: Fidelity, Diversity, and Bias Detection

• Chapter 4.4: Comparing Synthetic vs. Real Model Performance

• Chapter 5.1: Integrating Synthetic Data in Model Training Pipelines

• Chapter 5.2: Augmentation Strategies in Low-Data and Imbalanced Settings

• Chapter 5.3: Model Debugging and Adversarial Testing with Synthetic Scenarios

• Chapter 5.4: Federated Learning and Simulation Environments

• Chapter 6.1: Regulatory Considerations and Industry Standards

• Chapter 6.2: Transparency, Disclosure, and Responsible Use

• Chapter 6.3: Use Cases: Healthcare, Finance, Autonomous Systems

• Chapter 6.4: Capstone Project – Design and Evaluate a Synthetic Data Pipeline

~Video content aligned with weekly modules

Theme: Foundations and Generation Techniques

• What is Synthetic Data and Why It Matters

• Types of Synthetic Data: Tabular, Image, Text, Time-Series

• When to Use (and Avoid) Synthetic Data

• Overview of Tools: SDV, Gretel, MOSTLY AI, Unity Perception

• Generative Techniques: GANs, VAEs, Diffusion Models

• Prompt-Based Synthetic Text Generation with LLMs

• Data Preparation for Synthetic Modeling

Theme: Building and Evaluating Synthetic Data Pipelines

• Generating Tabular Data with Statistical and Probabilistic Models

• GANs for Image and Video Data Simulation

• Using LLMs for Task-Specific Text Synthesis

• Augmenting Minority Classes in Imbalanced Datasets

• Measuring Utility: Similarity, Predictive Parity, Model Transfer

• Measuring Privacy: Differential Privacy, K-Anonymity, MIA Defense

• Validating Bias and Fidelity in Synthetic Outputs

• Comparing Performance: Real vs. Synthetic Training Data

• End-to-End Tabular Generation and Testing Pipeline

Theme: Deployment, Ethics, and Industry Use Cases

• Using Synthetic Data in Production AI Workflows

• Synthetic Data for Simulation, Debugging, and Scenario Planning

• Regulatory Landscape: GDPR, HIPAA, and ISO Guidelines

• Transparency and Disclosure in AI Products Using Synthetic Data

• Federated and Synthetic Learning for Privacy-Preserving AI

• Case Study: Synthetic Data in Healthcare AI

• Case Study: Synthetic Data for Computer Vision in Automotive

• Capstone Briefing: Building and Evaluating Your Synthetic Dataset

• Final Recap and Future Trends in Synthetic Data Generation

Title: Generating the Future: The Role of Synthetic Data in Modern AI
Duration: 60 minutes
Focus: Overview of synthetic data’s purpose, scope, and technical foundations
Guest: Research Scientist specializing in generative models or privacy-preserving ML
Interactive: Tool showcase and live demo: creating synthetic tabular data for a toy problem

Title: Quality Matters: Validating Synthetic Data for Performance and Privacy
Duration: 75 minutes
Focus: Utility, fidelity, bias, and privacy trade-offs when using synthetic data in real pipelines
Guest: Data Scientist from an applied AI team (e.g., healthcare, finance, or autonomous systems)
Interactive: Group activity: critique and score a synthetic dataset on realism, risk, and model value

Title: From Sandbox to Scale: Deploying Synthetic Data in Production AI
Duration: 90 minutes
Focus: Best practices, governance, and ethical constraints when using synthetic data at scale
Guest Panel: AI Ethics Advisor + MLOps Engineer + Legal/Compliance Expert
Interactive: Capstone reviews + Q&A on regulatory risk and enterprise readiness

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