Creating and Validating Synthetic Financial Data in Python - Part 1

Part 1 of a 2 part video series showing you how to generate, validate, and assess synthetic financial data using Python. You’ll learn how to test for analytical fidelity, apply privacy safeguards, and ultimately identify which model is the best choice for your dataset. Tutorial Link: https://datasense.to/2025/09/13/synthetic-financial-data-python-guide/ Gitlab Repo: https://gitlab.com/datasense-co/synthetic-financial-data Timestamps: 0:45 - Reviewing the Data 2:30 - Data Cleaning and Transformation 4:15 - Exploratory Data Analysis (EDA) 5:20 - Prerequisites 5:40 - Discussing Domain Constraints and Gaussian Copula 8:30 - Metadata and Domain Constraints 13:00 - Generating the Synthetic Dataset with Gaussian Copula and Domain Constraints 15:00 - Quick Checks (EDA) of the Synthetic Data 19:25 - Analytical Fidelity Testing (Gaussian Copula -- Synthetic Dataset) 24:25 - Privacy Preservation 29:50 - Model Sweep for Initial Model Selection 38:10 - Analytical Fidelity Testing (CTGAN -- Synthetic Dataset) Contact Us: https://datasense.to/#contact #syntheticdata #syntheticfinancialdata #financialservices #datasense #dataprivacy #differentialprivacy #kanonymity #gaussiancopula #pythontutorial #sdvpython #datascience #financialriskmanagement #datavalidation #datasynthesis #regulators #fintech #economics #supervisorytechnology #regtech #dataanalysis #privacypreservingdata