page_title: Data visualization
section_title: Overview
hierarchical_section_title: Data visualization / Overview
caption_attribution_description: English: Flowchart showing the data visualization process. Diagram overview This diagram shows the data science process. Data is collected from sensors in the environment, represented by the globe. Data is "cleaned" or otherwise processed to produce a data set (typically a data table) usable for processing. Exploratory data analysis and statistical modeling may then be performed. A "data product" is a program such as retailers use to suggest new purchases based on purchase history. It can also create data and feed it back into the environment. This diagram is based on a similar diagram in "Doing Data Science" by O'Neill and Schutt (2014).&#91;1&#93; Gil Press discussed the history of data science and related terminology in a Forbes article in May 2013.&#91;2&#93; References ↑ O'Neil, Cathy and, Schutt, Rachel (2014) Doing Data Science, O'Reilly ISBN&#58; 978-1-449-35865-5.&#32; ↑ Forbes-Gil Press-A Very Short History of Data Science-May 2013
context_page_description: Data visualization is the graphic representation of data. It involves producing images that communicate relationships among the represented data to viewers of the images. This communication is achieved through the use of a systematic mapping between graphic marks and data values in the creation of the visualization. This mapping establishes how data values will be represented visually, determining how and to what extent a property of a graphic mark, such as size or color, will change to reflect changes in the value of a datum.
  To communicate information clearly and efficiently, data visualization uses statistical graphics, plots, information graphics and other tools. 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 follows the task.
context_section_description: Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in data analysis or data science. According to Vitaly Friedman (2008) the "main goal of data visualization is to communicate information clearly and effectively through graphical means. It doesn't mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key-aspects in a more intuitive way. Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information".
  Indeed, Fernanda Viegas and Martin M. Wattenberg suggested that an ideal visualization should not only communicate clearly, but stimulate viewer engagement and attention.
  Data visualization is closely related to information graphics, information visualization, scientific visualization, exploratory data analysis and statistical graphics. In the new millennium, data visualization has become an active area of research, teaching and development. According to Post et al. (2002), it has united scientific and information visualization.
caption: Data visualization is one of the steps in analyzing data and presenting it to users.
exif: {}