 Computational statistics, or statistical computing, is the interface between statistics and computer science. It is the area of computational science or scientific computing specific to the mathematical science of statistics. This area is also developing rapidly, leading to calls fed a broader concept of computing should be taught as part of general statistical education. As in traditional statistics the goal is to transform raw data into knowledge, but the focus lies on computer intensive statistical methods, such as cases with very large sample size and non-homogeneous data sets. The terms computational statistics and statistical computing are often used interchangeably, although Carlo Loro, a former president of the International Association for Statistical Computing proposed making the distinction, defining statistical computing as the application of computer science to statistics and computational statistics as aiming at the design of algorithm for Implementing statistical methods on computers, including the ones unthinkable before the computer age e.g. bootstrap, simulation, as well as to cope with analytically intractable problems. The term computational statistics may also be used to refer to computationally intensive statistical methods including resampling methods, Markov chain Monte Carlo methods, local regression, kernel density estimation, artificial neural networks and generalized additive models.