Generating Synthetic Data with Python | by Iffat Malik Gore | Jul, 2023


A Comprehensive Guide to Creating Synthetic Data

Iffat Malik Gore

Towards Data Science

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We keep hearing time and again about the pivotal role that data plays in driving growth, innovation, and competitiveness. It has become the bedrock for success across all industries. In essence, data has become the foundation of our every endeavour, from crafting technical blogs, educational content, and testing products or debugging software to explore the complexities of AI/ML training models and algorithms, data lies at the heart of all these tasks.

Obtaining precise data that perfectly fit various needs and interests can be a Herculean task. Searching the internet for the exact data you need can be both frustrating and time-consuming. Even if you manage to find suitable data, the process of cleaning and processing it may demand valuable time, resources, and expenses. Moreover, privacy concerns, data sensitivity, copyrights, and regulatory restrictions often stand as significant barriers. For example, datasets containing sensitive information like medical data, financial records data, or obtaining a demo dataset from a copyrighted website, etc.

In situations like these, synthetic data comes to save the day! In this article, we’ll explore what synthetic data is all about and how you can generate it in Python using 2 different libraries.

What is synthetic data?

Synthetic data, according to Wikipedia, is data that is artificially generated instead of being derived from real-world events. In the simplest language,

Synthetic Data = Fake Data

It is a replication of real data that may maintain its resemblance without disclosing any specific information about real individuals, situations, or entities. You might have already heard different terms, including computer-generated data, artificial data, AI-generated data, or simulated data, but essentially, they all are more or less the same – Fake Data.

Why is synthetic data required?

You may wonder why we need synthetic data when we already have plenty of real-world data. It is valuable for various reasons, it allows us to create additional data that looks like real data but doesn’t…



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