Difference between revisions of "Data visualization"
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'''Data visualization''' or '''data visualisation''' is the creation and study of the visual representation of [[data]], meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information". | '''Data visualization''' or '''data visualisation''' is the creation and study of the visual representation of [[data]], meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information". | ||
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| + | Compare [[Information visualization]] and [[Scientific visualization]]. | ||
== Description == | == Description == | ||
Revision as of 13:46, 22 May 2016
Data visualization or data visualisation is the creation and study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information".
Compare Information visualization and Scientific visualization.
Contents
Description
A primary goal of data visualization is to communicate information clearly and efficiently via statistical graphics, plots and information graphics.
Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message.
Effective visualization helps users analyze and reason about data and evidence.
It makes complex data more accessible, understandable and usable.
Users may have particular analytical tasks, such as making comparisons or understanding causality, and the design principle of the graphic (i.e., showing comparisons or showing causality) follows the task.
Tables are generally used where users will look up a specific measurement, while charts of various types are used to show patterns or relationships in the data for one or more variables.
Data visualization is both an art and a science.
It is viewed as a branch of descriptive statistics by some, but also as a grounded theory development tool by others.
The rate at which data is generated has increased.
Data created by Internet activity and an expanding number of sensors in the environment, such as satellites, are referred to as Big Data.
Processing, analyzing and communicating this data present a variety of ethical and analytical challenges for data visualization.
The field of data science and practitioners called data scientists have emerged to help address this challenge.
See also
- Analytics
- Balanced scorecard
- Business analysis
- Business intelligence
- Computer art
- Computer science
- Computing
- Data
- Data analysis
- Data profiling
- Data warehouse
- Exploratory data analysis
- Infographic
- Information architecture
- Information design
- Information visualization
- Interaction design
- Interaction techniques
- Mathematical beauty
- Mathematics and art
- Scientific visualization
- Software visualization
- Statistical analysis
- Statistical graphics
- Visual analytics
- Visual arts
People (Historical)
People (active today)
- Albero Cairo
- Edward Tufte
- Ola Rosling - Rosling developed the scatter-plot graphing tool used on Gapminder.org.
- Hans Rosling
- Aaron Koblin
- Manuel Lima
- Max Roser - Roser is an economist at the University of Oxford and author of the online data visualisation publication Our World In Data.
- Moritz Stefaner
- Ben Shneiderman
- Fernanda Viégas
- Martin M. Wattenberg
- Mona Chalabi - Data journalist at FiveThirtyEight. Previously at the Guardian, the Bank of England, and the Economist Intelligence Unit.
- George Furnas
- Branko Milanovic
- Mike Bostock - Bostock is one of the key developers of the Javascript library D3.js
- Adrien Segal - Oakland, CA based artist known for her sculptures based on tidal and snow data.
External links
- Data visualization @ Wikipedia
