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Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data." [3] Exploratory data ...
Silicon Insight - Interactive program that works with 3rd party testers for controlling and logging of data on the device for at-speed testing; Yield Insight - Yield analysis program which looks at foundry and performance data to diagnose possible problems; HDL Designer Series; Through Solido Design Automation Acquisition
Pages in category "Exploratory data analysis" The following 13 pages are in this category, out of 13 total. This list may not reflect recent changes. ...
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Data exploration is an approach similar to initial data analysis, whereby a data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data, rather than through traditional data management systems. [1]
A good quote from J. Heer and B. Schneirderman paper is “visual-analysis systems can incorporate guided analytics to lead analysts through workflows for common tasks”. [2] In 2016, vendors like Qlik and Tableau proposed guided analytics for exploratory data analysis tasks. [6]
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.
Event-driven architecture (EDA) is a software architecture paradigm concerning the production and detection of events. Event-driven architectures are evolutionary in nature and provide a high degree of fault tolerance, performance, and scalability. However, they are complex and inherently challenging to test. EDAs are good for complex and ...