Data estimation is a research area within statistics and mathematics that focuses on techniques for estimating unknown parameters or characteristics of a population based on a sample of data. This field is important in various scientific disciplines and is used to make predictions, draw conclusions, and make decisions based on limited information. Data estimation involves developing mathematical models, algorithms, and statistical methods to accurately estimate unknown parameters such as mean, variance, regression coefficients, and probabilities. Common techniques used in data estimation include maximum likelihood estimation, Bayesian estimation, and method of moments. Overall, data estimation plays a crucial role in data analysis, inferential statistics, and making informed decisions based on data.