In computer science, data is the combination of mathematical descriptions or numerical features, generally numerical, which are gathered through observation. In a less technical sense, data may be a collection of values of any kind, of any type, for example, the set of points on a graph, the sum of all the values of a particular variable, or the sum of all the values of a particular quantity.
Information can be measured in terms of these values, but it is also more generally used to refer to all of the information that is relevant to the process of inquiry. For example, the amount of water in a bottle is measured in terms of the volume, while the amount of salt is measured in terms of the density.
In everyday life, the meaning of data is mostly intuitive. What is the meaning of water? Water is a gas and a liquid at the same time, and it has certain properties such as solubility and is a very good conductor of heat.
Data also have logical meanings. Data are organized in terms of their relationships, so that a given sequence of events is considered to be a set, and not just a random series of events that happen to have happened.
Data is the basis of many scientific disciplines, including statistical analysis. In statistical analysis, the statistical description of a given situation is made by grouping similar events together to form a statistical record. The statistical record may then be compared to a model (or a series of models) in order to predict an outcome.
Data can be used in a variety of ways. There are different kinds of data, and different ways of analyzing them:
Data from experiments (experimental data) is used in experiments and analysis. It may either be quantitative or qualitative, depending on whether the information is considered quantitative or qualitative. Experiments may be conducted on animals, humans, or both, with different kinds of subjects. Data collected during a scientific experiment (statistical data) is normally used for future scientific research.
Data gathered from observations (numerical data) is used to infer the future or present state of the world. It may be qualitative or quantitative, depending on the subject. Data collected from nature (physical data) can be used to create new knowledge and predictions or to help us better understand the natural world.
Data obtained from computer programs (computer data) is used in research and studies that study the way information is transmitted between the brain and the computer. This kind of data is usually categorized into two types: analog data and digital data. Analog data consists of measurements of physical characteristics, while digital data are the measurements of information.
Data taken from real-world situations are called qualitative data. They can be described in terms of the results of observation or experimentation, or through observation of a specific problem or a group of situations. This kind of data can also be described by data collected through experiments, but these data are often qualitative and cannot be compared with mathematical models.
Data obtained from simulations is called quantitative data. These data are based on the experimental data. The data can be described in terms of a series of numbers. Some people consider these data to be the most important of all kinds of data.
Data that describe the behavior of a system can also be called models, since they represent a particular way to use a particular system. Models can describe the characteristics of a system and how to use the system to get the desired result. When data is considered in a mathematical model, the data is often referred to as a prediction of the behavior of the system. The prediction of the model can then be used as input into a model, and the predicted model used as input into an experiment.
Data can also be considered as a form of knowledge, since the model that contains the information about the model is itself a piece of information. For example, in the case of the model of a system, a data record containing information about the properties of the system can be considered as data. The information can be used to make a prediction, or used to explore the model by following the path of the model and testing it against the data.