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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]
However, this technique has been shown to yield poorly calibrated models, with an overestimated probability to belong to the minority class. [5] To illustrate how this technique works consider some training data which has s samples, and f features in the feature space of the data. Note that these features, for simplicity, are continuous.
Exploratory data analysis is an analysis technique to analyze and investigate the data set and summarize the main characteristics of the dataset. Main advantage of EDA is providing the data visualization of data after conducting the analysis.
The scope of the discipline of statistics broadened in the early 19th century to include the collection and analysis of data in general. Today, statistics is widely employed in government, business, and natural and social sciences. Carl Friedrich Gauss made major contributions to probabilistic methods leading to statistics.
Data processing is the collection and manipulation of digital data to produce meaningful information. [1] Data processing is a form of information processing , which is the modification (processing) of information in any manner detectable by an observer.
The Latin word data is the plural of datum, "(thing) given," and the neuter past participle of dare, "to give". [6] The first English use of the word "data" is from the 1640s. The word "data" was first used to mean "transmissible and storable computer information" in 1946. The expression "data processing" was first used in 1954. [6]
Data literacy is the ability to read, understand, create, and communicate data as information. Much like literacy as a general concept, data literacy focuses on the competencies involved in working with data. It is, however, not similar to the ability to read text since it requires certain skills involving reading and understanding data. [1]
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...