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What is synthetic data?

Synthetic data refers to artificially generated data that mimics the statistical properties and patterns of real-world data.

Why should I use synthetic data?

Cost-Effective: Generating synthetic data is often cheaper and more time-efficient than collecting and labeling real-world data.

Private: Synthetic data can help to protect individual privacy by creating fictitious data that resembles real-world data but does not contain any sensitive information.

Diverse: Synthetic data can be used to create diverse datasets that capture a wider range of scenarios and outliers than real-world data.

Controllable: Synthetic data allows researchers to control the distribution of the data and manipulate it to create specific scenarios, which can be valuable in testing hypotheses or training models.

Scalable: Synthetic data can be generated at scale, making it useful for applications such as training machine learning models, testing algorithms, and simulating complex systems.

Who uses synthetic data?

Synthetic data has the potential to revolutionize many fields, from healthcare and finance to transportation and cybersecurity, by providing a cost-effective and flexible way to generate large and diverse datasets for analysis, modeling, and more.

How do I get started with synthetic data?

The best way to learn about synthetic data is to experiment with synthetic data generation.

Generate your first synthetic dataset based on your original data for free at

No coding or advanced data science knowledge needed!