Simulated Data

Simulated data refers to artificially generated datasets that mimic the statistical properties and patterns of real-world data, created using algorithms, models, or random processes. It is commonly used in software development, data science, and testing to prototype systems, validate models, or ensure privacy when real data is unavailable or sensitive. This approach allows developers to work with realistic data scenarios without the risks or limitations associated with actual data.

Also known as: Synthetic Data, Artificial Data, Mock Data, Fake Data, Generated Data

Why learn Simulated Data?

Developers should learn and use simulated data when real data is scarce, expensive to obtain, or contains sensitive information, such as in healthcare or finance applications. It is essential for testing software under various conditions, training machine learning models in controlled environments, and conducting simulations for research or system design, ensuring robustness and compliance with data privacy regulations like GDPR or HIPAA.

Learning Resources

• tutorial: Synthetic Data: What, Why, and How?
• tutorial: Generating Synthetic Data with Python
• docs: Simulated Data in R: A Practical Guide
• course: Synthetic Data for Machine Learning
• video: Mock Data Generation Tools and Techniques