Chemometrics is the science of extracting information from chemical systems by data-driven means. It is a highly interfacial discipline, using methods frequently employed in core data-analytic disciplines such as multivariate statistics, applied mathematics, and computer-science, but to investigate and address problems in chemistry, biochemistry and chemical engineering.
Chemometrics is applied to solve both descriptive and predictive problems in chemistry. In descriptive applications, properties of chemical systems are modeled with the intent of learning the underlying relationships and structure of the system (i.e., model identification). In predictive applications, properties of chemical systems are modeled with the intent of predicting new properties or behavior of interest. In both cases, the datasets are often very large and highly complex, involving hundreds to tens of thousands of variables, and hundreds to millions of cases or observations.