• Understand the concept and need for synthetic data.

• Learn the differences between real, augmented, and simulated data.

• Implement rule-based and AI-based data generation techniques.

• Build and train GANs, VAEs, and Diffusion Models.

• Generate synthetic text, tabular, and image data.

• Apply synthetic data for data augmentation and model generalisation.

• Ensure privacy and bias mitigation in synthetic datasets.

• Evaluate data realism and statistical accuracy.

• Integrate synthetic data pipelines into ML workflows.

• Prepare for roles in data engineering and AI innovation.

Course Syllabus

Module 1: Introduction to Synthetic Data and Its Applications
Module 2: Statistical Modelling and Data Simulation
Module 3: Generative Models – GANs, VAEs, and Diffusion Networks
Module 4: Data Augmentation and Transformation Techniques
Module 5: Synthetic Text, Image, and Tabular Data Generation
Module 6: Privacy Preservation and Bias Reduction
Module 7: Tools and Frameworks – SynthCity, Gretel, SDV, and Faker
Module 8: Evaluation Metrics for Synthetic Data Utility
Module 9: Industry Case Studies – Healthcare, Finance, and Robotics
Module 10: Capstone Project – Build a Synthetic Data Generation Pipeline

Upon successful completion, learners receive a Certificate of Completion from Uplatz, confirming their expertise in Synthetic Data Generation. This Uplatz certification validates your ability to design, build, and evaluate synthetic data solutions that enhance AI performance while ensuring privacy and fairness.

The certification aligns with global trends in AI ethics, data governance, and responsible innovation. It is ideal for data scientists, ML engineers, and compliance professionals seeking to overcome real-world data challenges using synthetic approaches.

Earning this certification demonstrates your readiness to work on advanced AI projects that require secure, scalable, and high-quality synthetic datasets.

With industries prioritising data privacy and compliance, Synthetic Data Engineers are becoming highly sought-after professionals. Completing this course from Uplatz prepares you for positions such as:

• Synthetic Data Scientist

• Data Simulation Engineer

• Privacy-Preserving AI Specialist

• AI Research Scientist

• Data Quality Engineer

Professionals in this domain typically earn between $105,000 and $185,000 per year, depending on their industry and level of expertise.

Career opportunities are expanding in healthcare, autonomous systems, financial technology, and cybersecurity — sectors where generating high-fidelity data without privacy compromise is essential. This course empowers you to create trustworthy datasets that accelerate innovation across the AI ecosystem.

1. What is synthetic data?
   Artificially generated data that replicates the statistical properties of real-world data.

2. Why is synthetic data important?
   It enables model training without privacy violations or reliance on scarce data.

3. How is synthetic data generated?
   Using statistical simulation, generative AI (GANs, VAEs), or rule-based synthesis.

4. What are GANs?
   Generative Adversarial Networks — AI models with generator and discriminator components that create realistic synthetic data.

5. How does synthetic data improve privacy?
   It removes personal identifiers and replaces sensitive records with generated equivalents.

6. What are common use cases?
   Healthcare research, fraud detection, autonomous vehicles, and finance.

7. How do you evaluate synthetic data quality?
   By comparing statistical similarity and model performance on real vs synthetic datasets.

8. What tools are popular for synthetic data generation?
   SDV, Gretel, SynthCity, and Microsoft Presidio.

9. What are potential drawbacks?
   Over-fitting to training data, loss of diversity, and reduced utility if poorly generated.

10. How is synthetic data used in deep learning?
    To augment datasets, balance classes, and improve model generalisation.