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Data presentation architecture weds the science of numbers, data and statistics in discovering valuable information from data and making it usable, relevant and actionable with the arts of data visualization, communications, organizational psychology and change management in order to provide business intelligence solutions with the data scope ...
Edward Rolf Tufte was born in 1942 in Kansas City, Missouri, to Virginia Tufte (1918–2020) and Edward E. Tufte (1912–1999). He grew up in Beverly Hills, California, where his father was a longtime city official.
The Information visualization reference model is an example of a reference model for information visualization, developed by Ed Chi in 1999, [1] under the name of the data state model. Chi showed that the framework successfully modeled a wide array of visualization applications and later showed that the model was functionally equivalent to the ...
Software visualization [1] [2] or software visualisation refers to the visualization of information of and related to software systems—either the architecture of its source code or metrics of their runtime behavior—and their development process by means of static, interactive or animated 2-D or 3-D [3] visual representations of their structure, [4] execution, [5] behavior, [6] and evolution.
Scientific visualization focuses and emphasizes the representation of higher order data using primarily graphics and animation techniques. [ 5 ] [ 6 ] It is a very important part of visualization and maybe the first one, as the visualization of experiments and phenomena is as old as science itself.
Data science is an interdisciplinary academic field [1] that uses statistics, scientific computing, scientific methods, processing, scientific visualization, algorithms and systems to extract or extrapolate knowledge from potentially noisy, structured, or unstructured data.
Data exploration can also refer to the ad hoc querying or visualization of data to identify potential relationships or insights that may be hidden in the data and does not require to formulate assumptions beforehand. [1] Traditionally, this had been a key area of focus for statisticians, with John Tukey being a key evangelist in the field. [5]
Real data is always finite, and so its study requires us to take stochasticity into account. Statistical analysis gives us the ability to separate true features of the data from artifacts introduced by random noise. Persistent homology has no inherent mechanism to distinguish between low-probability features and high-probability features.
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