The Starters Guide To Learn Generating Synthetic Data: Sampling From Univariate Distributions

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The Starters Guide To Learn Generating Synthetic Data: Sampling From Univariate Distributions

Learn how to generate synthetic data when real-world data is scarce or when only expert knowledge is available. A practical guide combining theory, code, and real applications.

Data is the fuel in Data Science projects. But what if the observations are scarce, expensive, or difficult to measure? Synthetic data can be the solution. Synthetic data is artificially generated data that mimics the statistical properties of real-world events. In this blog, you will learn how to generate continuous synthetic data by sampling from univariate distributions. First, we will go through probability distributions on how to specify the parameters to generate synthetic data. After that, we will generate data that mimics the properties of an existing data set, i.e., the random variables that are distributed according to a probabilistic model. All examples are created using scipy and the distfit library.