Programme Parameters
Graduate / Intermediate
3 Weeks (30 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Synthetic Data Generation & Use in AI is an applied program designed for data scientists, ML engineers, and AI practitioners who face limitations with real-world datasets. The course explores how synthetic data—artificially generated but statistically accurate—can overcome data scarcity, improve privacy, and boost the robustness of AI models. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Synthetic Data Generation & Use in AI is an applied program designed for data scientists, ML engineers, and AI practitioners who face limitations with real-world datasets. The course explores how synthetic data—artificially generated but statistically accurate—can overcome data scarcity, improve privacy, and boost the robustness of AI models.
📋 Course Objectives
1. Apply biotechnology methods to authentic research and industry problems.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• A demonstrable biotechnology project for your research or industry portfolio.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Introduction to Synthetic Data
Define synthetic data and distinguish its types including tabular, image, text, and time-series formats • Analyze the benefits of synthetic data over real data in terms of privacy, cost, and scalability • Evaluate scenarios to determine when and when not to use synthetic data in AI projects
Tools and Techniques for Data Generation
Explore leading synthetic data generators including Gretel, MOSTLY AI, and SDV • Implement GANs, VAEs, and LLMs for generating high-fidelity synthetic datasets • Apply prompt-based data synthesis techniques for NLP and domain-specific tasks
Generating Synthetic Data
Build GAN-based generation pipelines for synthetic images and video content • Generate synthetic tabular data using statistical models and simulation frameworks • Balance and augment existing datasets with strategically synthesized samples
Evaluation and Quality Assurance
Measure utility metrics to assess how useful synthetic data is for downstream AI tasks • Implement privacy metrics including differential privacy, k-anonymity, and membership inference tests • Detect fidelity gaps, diversity limitations, and hidden biases in generated datasets
Deploying Synthetic Data in AI Workflows
Integrate synthetic data seamlessly into model training and validation pipelines • Design augmentation strategies for low-data and imbalanced classification scenarios • Conduct adversarial testing and model debugging using synthetic scenario generation
Ethics, Governance, and Real-World Impact
Navigate regulatory considerations and emerging industry standards for synthetic data use • Practice transparency, disclosure, and responsible deployment in AI systems • Complete a capstone project designing and evaluating a full synthetic data pipeline
Advanced Generative Models and Diffusion Techniques
Harness diffusion models for high-quality synthetic image and multimodal data generation • Fine-tune large language models for domain-specific synthetic text corpus creation • Optimize generative pipelines for computational efficiency and output quality
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Gretel.ai |
| Covered Tool / Platform | MOSTLY AI |
| Covered Tool / Platform | SDV |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Hugging Face |
| Covered Tool / Platform | Diffusers |
| Covered Tool / Platform | OpenAI API |
| Covered Tool / Platform | Differential Privacy libraries |
Frequently Asked Questions
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