As already mentioned above, try to use generative AI methods to generate synthetic data. We can generate data according to the necessary conditions, so that it provides enough data for our analysis.

When dealing with limited datasets ensuring diversity in data sampling requires being resourceful. You start by looking for extra data from different sources even those you wouldn’t normally consider. This helps in bringing more variety into the sample. You can also split your data into meaningful groups and make sure to include examples from each group so you’re not missing any important representation. In cases where data is really scarce creating synthetic data that mimics real world conditions can help fill the gaps, making the sample more well rounded without risking privacy issues.

Data sampling is critical to avoid bias and enhance the representativeness of insights. Diversity in data sampling is essential. Some key strategies to achieve diversity in your data sampling: Stratified Sampling: Involves dividing the dataset into subgroups (strata) based on key characteristics (e.g., age, region) and then sampling from each subgroup proportionally. Resampling Techniques: To create multiple samples, use techniques like bootstrapping, which involves repeatedly drawing random samples with replacements from the dataset. Data Augmentation: It is generating synthetic data or creating variations of existing data points. This can be useful in areas like image recognition or NLP to simulate more diversity.

To ensure diversity in data sampling with limited datasets: 1.Explore external data sources 2.Use stratified sampling 3.Leverage data augmentation 4.Adapt patterns from related domains 5.Prioritize data quality This strategy helps me maintain diversity in sampling, even when working with limited data.

Generating synthetic data is a great choice for this type of case. Combining it with feature engineering, data augmentation, and other additional databases can solve 90% of the problems in this type of situation.