Synthetic Data Generation: Applications And Challenges For Data Scientists

Synthetic data holds importance simply because it can be generated to meet particular needs or conditions that may not be available in the already existing'real' data. What this means is that in cases where a business might be looking for data that meets particular requirements/specifications. This synthetic data is available to cater to those particular needs. As we now know, these datasets are generated through computer programs rather than the documentation of real-world events. The primary aim is to create datasets that are versatile and robust enough to be useful in the training of machine learning models i.e. to ensure that the computing systems learn exactly the kind of information that the user wants it to work with.