Have you ever wondered what the difference is between synthetic data and mock data? In this video, we'll provide you with an objective analysis of the quantitative differences between these two data generation methods. This knowledge can empower you to make informed decisions when working with diverse data types in your data science and analytics projects.

Here is what you will learn:

00:09 - The Robustness of AI-Generated Synthetic Data
Discover why AI-generated synthetic data stands out as a robust tool, maintaining correlations between variables, and how this factor enhances its utility for data scientists and analysts.

01:00 - Get Hands-on with Data
We'll use the US Census Income data set, featuring attributes such as age, education, relationship, and occupation, to compare the characteristics of mock data and AI-generated synthetic data.

02:03 - Creating Mock Data
Generating mock data, employing industry-standard techniques. We'll use mean and standard deviations for numerical columns and adhere to business logic for categorical columns.

03:13 - Generating Synthetic Data
AI-generated synthetic data creation using MOSTLY AI's platform.

04:36 - Analyzing Correlation Matrices
We'll delve into correlation matrices for each data set to objectively visualize the relationships among variables, showcasing the significance of maintaining correlations.

06:09 - Comparative Analysis
Compare the correlation matrices of the original data, synthetic data, and mock data. Observe how well each data type preserves variable relationships.

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