Synthetic Data Generation & Use in AI

Unlock Data Innovation—Generate, Simulate, and Scale AI with Synthetic Data.

MODE
Mode(Online) TYPE
Mentor Based LEVEL
Moderate

Skills you will gain:

Synthetic Data Generation & Use in AI is an applied course designed for data scientists, ML engineers, and AI practitioners who face limitations with real-world datasets. The program explores how synthetic data—artificially generated but statistically accurate—can overcome data scarcity, improve privacy, and boost the robustness of AI models. Participants will learn generation techniques (GANs, simulations, diffusion models), evaluate data utility and privacy, and apply synthetic data to real AI workflows.

Aim:

To equip learners with the theoretical understanding and practical skills needed to generate, validate, and deploy synthetic data for AI development—enhancing model training, privacy protection, and data diversity in low-data or sensitive environments.

Program Objectives:

To enable secure, bias-mitigated data innovation using synthetic data
To reduce reliance on costly, restricted, or imbalanced real-world datasets
To build competency in cutting-edge generative models and simulation tools
To promote responsible AI through privacy-first data practices

What you will learn?

Week 1: Foundations of Synthetic Data and Its Role in AI

Module 1: Introduction to Synthetic Data

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

Module 2: Tools and Techniques for Data Generation

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

Week 2: Building and Validating Synthetic Data Pipelines

Module 3: Generating Synthetic Data

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

Module 4: Evaluation and Quality Assurance

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

Week 3: Operationalization, Ethics, and Use Cases

Module 5: Deploying Synthetic Data in AI Workflows

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

Module 6: Ethics, Governance, and Real-World Impact

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

Intended For :

Data scientists, ML/AI engineers, researchers, and data engineers
Professionals in healthcare, finance, robotics, or sensitive data domains
Knowledge of Python, ML frameworks, and basic statistics is recommended