17 Facts About Data analysis

Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.

Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains.

In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination.

Data analysis, is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users.

Data analysis, is collected and analyzed to answer questions, test hypotheses, or disprove theories.

Data analysis cleaning is the process of preventing and correcting these errors.

Data analysis visualization is a technique used, in which the analyst is able to examine the data in a graphical format in order to obtain additional insights, regarding the messages within the data.

Data analysis product is a computer application that takes data inputs and generates outputs, feeding them back into the environment.

Data analysis visualization uses information displays to help communicate key messages contained in the data.

Effective Data analysis requires obtaining relevant facts to answer questions, support a conclusion or formal opinion, or test hypotheses.

Data analysis emphasized procedures to help surface and debate alternative points of view.

The different steps of the data analysis process are carried out in order to realise smart buildings, where the building management and control operations including heating, ventilation, air conditioning, lighting and security are realised automatically by miming the needs of the building users and optimising resources like energy and time.

Choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase.

Nonlinear analysis is often necessary when the data is recorded from a nonlinear system.

Nonlinear data analysis is closely related to nonlinear system identification.

An exploratory Data analysis is used to find ideas for a theory, but not to test that theory as well.