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Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. [ 1] Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science ...
e. In statistics, exploratory data analysis (EDA) is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling and thereby contrasts ...
A data processing system is a combination of machines, people, and processes that for a set of inputs produces a defined set of outputs. The inputs and outputs are interpreted as data, facts, information etc. depending on the interpreter's relation to the system. A term commonly used synonymously with data or storage (codes) processing system ...
Business intelligence (BI) consists of strategies and technologies used by enterprises for the data analysis and management of business information. [1] Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining ...
A data model provides the details of information to be stored, and is of primary use when the final product is the generation of computer software code for an application or the preparation of a functional specification to aid a computer software make-or-buy decision. The figure is an example of the interaction between process and data models.
v. t. e. Data and information visualization ( data viz/vis or info viz/vis) [ 2] is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount [ 3] of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items.
Symbolic data analysis ( SDA) is an extension of standard data analysis where symbolic data tables are used as input and symbolic objects are made output as a result. The data units are called symbolic since they are more complex than standard ones, as they not only contain values or categories, but also include internal variation and structure ...
Data engineering. Data engineering refers to the building of systems to enable the collection and usage of data. This data is usually used to enable subsequent analysis and data science; which often involves machine learning. [ 1][ 2] Making the data usable usually involves substantial compute and storage, as well as data processing .