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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]
Traffic analysis of Wikipedia. Analytics is the systematic computational analysis of data or statistics. [1] It is used for the discovery, interpretation, and communication of meaningful patterns in data, which also falls under and directly relates to the umbrella term, data science. [2]
Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. [5] It uses techniques and theories drawn from many fields within the context of mathematics , statistics, computer science , information science , and domain knowledge . [ 6 ]
D. Dark data; Data access; Data analysis for fraud detection; Data exploration; Data farming; Data fusion; Data profiling; Data science; Data version control; Data-driven model
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."
Data augmentation in data analysis are techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data. It acts as a regularizer and helps reduce overfitting when training a machine learning model. [8] (See: Data augmentation)
Distributional data analysis is a branch of nonparametric statistics that is related to functional data analysis.It is concerned with random objects that are probability distributions, i.e., the statistical analysis of samples of random distributions where each atom of a sample is a distribution.
It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions. [1] Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element.